{"as_of":"2026-08-13T22:42:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8c9e0a7c7805ff246cfa1126a155b9f0488f1f279259a7ba3dee904d359af0a6","coverage":[{"denominator":28,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":28,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T23:53:48.155699Z","state":"measured"},{"denominator":28,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":28,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2506.15853/citation-record","integrity":"/paper/2506.15853/integrity","json":"/paper/2506.15853/citation-record.json","paper":"/paper/2506.15853"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:53:54.871090Z","title":null,"venue":null,"work_id":"186aabb3-3fbb-41e5-9194-6bc9dd297da8","year":2014},"citing_paper":{"arxiv_id":"2506.15853","last_updated":"2025-06-18T20:01:14Z","snapshot_observed_at":"2026-08-13T03:04:22.467010Z","submitted_at":"2025-06-18T20:01:14Z","title":"Cross-Modality Learning for Predicting IHC Biomarkers from H&E-Stained Whole-Slide Images","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T23:53:45.032783Z"},"links":{"citing_paper":"/paper/2506.15853"},"observation_digest":"sha256:817b9e36fbf06bc16b3cc396f5bf9fe77d100e47745a58d8c4d2a71e4d2d0763","observation_id":"95c23d35-bdcb-4d7e-aafd-41c12b9f1ebe","resolution":{"observed_at":"2026-08-06T23:53:54.919245Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:53:54.663075Z","title":"A., Wei, B., So, A","venue":null,"work_id":"574ebfe3-2814-4c1f-af9b-0818e47b48f5","year":2019},"citing_paper":{"arxiv_id":"2506.15853","last_updated":"2025-06-18T20:01:14Z","snapshot_observed_at":"2026-08-13T03:04:22.467010Z","submitted_at":"2025-06-18T20:01:14Z","title":"Cross-Modality Learning for Predicting IHC Biomarkers from H&E-Stained Whole-Slide Images","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T23:53:45.161096Z"},"links":{"citing_paper":"/paper/2506.15853"},"observation_digest":"sha256:e20f786a4e3ba4e47babf8ff12ad9488cbbacaf2a174ef8ef259e23d2476954d","observation_id":"a0098504-dfe6-4bc7-a5cb-2f268143a46a","resolution":{"observed_at":"2026-08-06T23:53:54.792372Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:53:54.472930Z","title":null,"venue":null,"work_id":"df11d3c0-ed0a-4f25-ac68-86d860e37347","year":2024},"citing_paper":{"arxiv_id":"2506.15853","last_updated":"2025-06-18T20:01:14Z","snapshot_observed_at":"2026-08-13T03:04:22.467010Z","submitted_at":"2025-06-18T20:01:14Z","title":"Cross-Modality Learning for Predicting IHC Biomarkers from H&E-Stained Whole-Slide Images","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T23:53:45.228018Z"},"links":{"citing_paper":"/paper/2506.15853"},"observation_digest":"sha256:067209dcfa8f459f037a738adaa4539cb91808e9b7c10eebf8673391ab95c1d4","observation_id":"37d03998-fa61-47c5-aca0-63d2db6a4724","resolution":{"observed_at":"2026-08-06T23:53:54.559836Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:53:54.312877Z","title":null,"venue":null,"work_id":"39c96e2d-72b0-427c-a542-dcf68fbfb42b","year":2022},"citing_paper":{"arxiv_id":"2506.15853","last_updated":"2025-06-18T20:01:14Z","snapshot_observed_at":"2026-08-13T03:04:22.467010Z","submitted_at":"2025-06-18T20:01:14Z","title":"Cross-Modality Learning for Predicting IHC Biomarkers from H&E-Stained Whole-Slide Images","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T23:53:45.310056Z"},"links":{"citing_paper":"/paper/2506.15853"},"observation_digest":"sha256:7d733ce4bf30d3b8684f2ccdce1953323386a08902fa32a9444ac7df01ac72f2","observation_id":"3ad4c72f-d965-44af-9e03-73e056a7340a","resolution":{"observed_at":"2026-08-06T23:53:54.368786Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:53:54.038006Z","title":"& Krasinska, L","venue":null,"work_id":"15d22432-3477-4d95-8cb7-82fe61b1160e","year":2022},"citing_paper":{"arxiv_id":"2506.15853","last_updated":"2025-06-18T20:01:14Z","snapshot_observed_at":"2026-08-13T03:04:22.467010Z","submitted_at":"2025-06-18T20:01:14Z","title":"Cross-Modality Learning for Predicting IHC Biomarkers from H&E-Stained Whole-Slide Images","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T23:53:45.442123Z"},"links":{"citing_paper":"/paper/2506.15853"},"observation_digest":"sha256:f04ab8b3fadfaa4420d970ef6a1982530c8573f1e7a6dba3527c98f9e01717ba","observation_id":"c6c7a0bc-8519-43e5-9a28-409a8e95bc8e","resolution":{"observed_at":"2026-08-06T23:53:54.199899Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:53:53.735589Z","title":"& Ciompi, F","venue":null,"work_id":"7b4f067d-4490-49d1-9166-d5d59eeec399","year":2021},"citing_paper":{"arxiv_id":"2506.15853","last_updated":"2025-06-18T20:01:14Z","snapshot_observed_at":"2026-08-13T03:04:22.467010Z","submitted_at":"2025-06-18T20:01:14Z","title":"Cross-Modality Learning for Predicting IHC Biomarkers from H&E-Stained Whole-Slide Images","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T23:53:45.600812Z"},"links":{"citing_paper":"/paper/2506.15853"},"observation_digest":"sha256:6f7657e42f9fb55a482404a01303f095d8608fe2257e0ba5969a17a630b31f7a","observation_id":"842850c2-e75a-4d0e-addf-bd356affd7d9","resolution":{"observed_at":"2026-08-06T23:53:53.883980Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:53:53.459334Z","title":"J., Andor, N., Nguyen, Q","venue":null,"work_id":"8a1fbee2-7cb7-4e0d-ae0a-99d23969d59f","year":2022},"citing_paper":{"arxiv_id":"2506.15853","last_updated":"2025-06-18T20:01:14Z","snapshot_observed_at":"2026-08-13T03:04:22.467010Z","submitted_at":"2025-06-18T20:01:14Z","title":"Cross-Modality Learning for Predicting IHC Biomarkers from H&E-Stained Whole-Slide Images","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T23:53:45.696222Z"},"links":{"citing_paper":"/paper/2506.15853"},"observation_digest":"sha256:2bcafeaa33c25833c13a999ad81bd6b5310f7938b2b6bd515a681fa6887d3b44","observation_id":"423e0ab6-05c1-42c4-ab3e-688c9b3c5caa","resolution":{"observed_at":"2026-08-06T23:53:53.584113Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:53:53.173724Z","title":null,"venue":null,"work_id":"7ae8b00c-c914-4d70-ac78-8d9cc8e5fac1","year":2024},"citing_paper":{"arxiv_id":"2506.15853","last_updated":"2025-06-18T20:01:14Z","snapshot_observed_at":"2026-08-13T03:04:22.467010Z","submitted_at":"2025-06-18T20:01:14Z","title":"Cross-Modality Learning for Predicting IHC Biomarkers from H&E-Stained Whole-Slide Images","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T23:53:45.804288Z"},"links":{"citing_paper":"/paper/2506.15853"},"observation_digest":"sha256:8c48c79d293d81af63084458f2562235005e0e1eb489c735fbeba71b90533963","observation_id":"de19fe9f-54dc-45b4-baed-565f56171e2d","resolution":{"observed_at":"2026-08-06T23:53:53.295793Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:53:52.939447Z","title":null,"venue":null,"work_id":"02c27853-27a8-426e-b658-7fa8bbbadcf7","year":2022},"citing_paper":{"arxiv_id":"2506.15853","last_updated":"2025-06-18T20:01:14Z","snapshot_observed_at":"2026-08-13T03:04:22.467010Z","submitted_at":"2025-06-18T20:01:14Z","title":"Cross-Modality Learning for Predicting IHC Biomarkers from H&E-Stained Whole-Slide Images","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T23:53:45.924472Z"},"links":{"citing_paper":"/paper/2506.15853"},"observation_digest":"sha256:8df0919e527824b432fcac9b632ebac17bc9bac96c1bc9ffb84110ba7dc38e59","observation_id":"4857aae0-587a-4a0b-8106-6cda7f09771c","resolution":{"observed_at":"2026-08-06T23:53:53.053922Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:53:52.674279Z","title":null,"venue":null,"work_id":"8fed05fa-09ca-4da2-aea2-11c334cab751","year":2020},"citing_paper":{"arxiv_id":"2506.15853","last_updated":"2025-06-18T20:01:14Z","snapshot_observed_at":"2026-08-13T03:04:22.467010Z","submitted_at":"2025-06-18T20:01:14Z","title":"Cross-Modality Learning for Predicting IHC Biomarkers from H&E-Stained Whole-Slide Images","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T23:53:46.036935Z"},"links":{"citing_paper":"/paper/2506.15853"},"observation_digest":"sha256:2cd91dfda3212b9e47ea66a5ee18fda5cc3aae831e73430fb4d3bddf0e7ccf05","observation_id":"6dbaaa23-5bc0-4da2-adc6-cbff6005b13a","resolution":{"observed_at":"2026-08-06T23:53:52.831471Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:53:52.438118Z","title":"C., Nie, J., Liu, H., Song, Q., Yan, L","venue":null,"work_id":"83a19dbb-3836-4630-b0f5-477d0be989b9","year":2023},"citing_paper":{"arxiv_id":"2506.15853","last_updated":"2025-06-18T20:01:14Z","snapshot_observed_at":"2026-08-13T03:04:22.467010Z","submitted_at":"2025-06-18T20:01:14Z","title":"Cross-Modality Learning for Predicting IHC Biomarkers from H&E-Stained Whole-Slide Images","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T23:53:46.119759Z"},"links":{"citing_paper":"/paper/2506.15853"},"observation_digest":"sha256:fdbc83527c3607dc402312555eb50ca2f3db4a354d6b99eb3790d21d98a37a14","observation_id":"a439cdf8-d984-432a-8bf8-0c8501db26dc","resolution":{"observed_at":"2026-08-06T23:53:52.548959Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.03558","last_updated":"2023-12-06T15:40:28Z","snapshot_observed_at":"2026-08-13T07:17:18.952781Z","submitted_at":"2023-12-06T15:40:28Z","title":"When an Image is Worth 1,024 x 1,024 Words: A Case Study in Computational Pathology","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.03558","snapshot_observed_at":"2026-08-06T23:53:46.237484Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.15853","last_updated":"2025-06-18T20:01:14Z","snapshot_observed_at":"2026-08-13T03:04:22.467010Z","submitted_at":"2025-06-18T20:01:14Z","title":"Cross-Modality Learning for Predicting IHC Biomarkers from H&E-Stained Whole-Slide Images","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T23:53:46.237484Z"},"links":{"cited_paper":"/paper/2312.03558","citing_paper":"/paper/2506.15853"},"observation_digest":"sha256:395a0e7f6b873de913742b69fbcf799a1bb5bc36ccd9ceb9565cfff6a10cc213","observation_id":"8621f977-7b03-454d-8a35-8f7256c05e06","resolution":{"observed_at":"2026-08-06T23:53:46.237484Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:53:52.133385Z","title":"S., Workman, A","venue":null,"work_id":"19d28e36-7b97-4378-b929-edf1c65c263d","year":2025},"citing_paper":{"arxiv_id":"2506.15853","last_updated":"2025-06-18T20:01:14Z","snapshot_observed_at":"2026-08-13T03:04:22.467010Z","submitted_at":"2025-06-18T20:01:14Z","title":"Cross-Modality Learning for Predicting IHC Biomarkers from H&E-Stained Whole-Slide Images","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T23:53:46.316130Z"},"links":{"citing_paper":"/paper/2506.15853"},"observation_digest":"sha256:d784e1ceddf574312eb30e0c9ae11f537b848c539668af459f1a08eb337651dc","observation_id":"1cb1b6f3-150b-472c-99a9-b09099edda80","resolution":{"observed_at":"2026-08-06T23:53:52.268922Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:53:51.834675Z","title":"https://www.nature.com/articles/s41586-024-07441-w","venue":null,"work_id":"841d54be-32cf-4095-b2ef-56b4d18c8e97","year":null},"citing_paper":{"arxiv_id":"2506.15853","last_updated":"2025-06-18T20:01:14Z","snapshot_observed_at":"2026-08-13T03:04:22.467010Z","submitted_at":"2025-06-18T20:01:14Z","title":"Cross-Modality Learning for Predicting IHC Biomarkers from H&E-Stained Whole-Slide Images","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T23:53:46.413427Z"},"links":{"citing_paper":"/paper/2506.15853"},"observation_digest":"sha256:22450c53a3308fb87f7684611349ebdc2da220504e9260a76c23e6df883fffea","observation_id":"a10dc46f-f6ed-498c-b9b3-8843d88ea75d","resolution":{"observed_at":"2026-08-06T23:53:51.984577Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.48550/arxiv.2502.07409","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":null,"venue":"ArXiv.org","work_id":"3812b90e-b17b-436c-9fb6-c35c26c7cbe7","year":2025},"citing_paper":{"arxiv_id":"2506.15853","last_updated":"2025-06-18T20:01:14Z","snapshot_observed_at":"2026-08-13T03:04:22.467010Z","submitted_at":"2025-06-18T20:01:14Z","title":"Cross-Modality Learning for Predicting IHC Biomarkers from H&E-Stained Whole-Slide Images","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T23:53:46.554020Z"},"links":{"citing_paper":"/paper/2506.15853"},"observation_digest":"sha256:62f151d19df494d35e8e15aaa105afd5ce956c466228c6da865bc74e51d63d6a","observation_id":"6ad81b17-90c6-4fc3-9600-ea2ad698480a","resolution":{"observed_at":"2026-08-06T23:53:48.604348Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:53:51.444470Z","title":"PD-L1 IHC 22C3 pharmDx, Interpretation Manual, NSCLC 1% 50%","venue":null,"work_id":"9c55b73f-ab3b-4ae4-b5ee-05116de65718","year":null},"citing_paper":{"arxiv_id":"2506.15853","last_updated":"2025-06-18T20:01:14Z","snapshot_observed_at":"2026-08-13T03:04:22.467010Z","submitted_at":"2025-06-18T20:01:14Z","title":"Cross-Modality Learning for Predicting IHC Biomarkers from H&E-Stained Whole-Slide Images","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T23:53:46.668094Z"},"links":{"citing_paper":"/paper/2506.15853"},"observation_digest":"sha256:5ba80593c7c30513debbcea0672fada8a8387559d4b75bd052a3f00735fda23f","observation_id":"a22fbb97-3792-4146-a266-4d62aae41fbf","resolution":{"observed_at":"2026-08-06T23:53:51.638730Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:53:50.982423Z","title":"G., Hui, R., Csőszi, T., Fülöp, A","venue":null,"work_id":"84afcbcf-1f0f-4b14-a23a-8eb2392ce2dc","year":2016},"citing_paper":{"arxiv_id":"2506.15853","last_updated":"2025-06-18T20:01:14Z","snapshot_observed_at":"2026-08-13T03:04:22.467010Z","submitted_at":"2025-06-18T20:01:14Z","title":"Cross-Modality Learning for Predicting IHC Biomarkers from H&E-Stained Whole-Slide Images","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T23:53:46.751686Z"},"links":{"citing_paper":"/paper/2506.15853"},"observation_digest":"sha256:417592e6f4abda62cdff309cd47eb92e8b98f22d744925b0ff0a4cec8e1ac03e","observation_id":"01d83c5a-f7df-4abd-a682-0b0c6eb34d90","resolution":{"observed_at":"2026-08-06T23:53:51.217256Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:53:50.818251Z","title":"B., Wei, W., Gupta, S., Zugazagoitia, J., Robbins, C., Adamson, B","venue":null,"work_id":"f3faf5f1-9846-48c7-ab53-7a312c14c1c8","year":2021},"citing_paper":{"arxiv_id":"2506.15853","last_updated":"2025-06-18T20:01:14Z","snapshot_observed_at":"2026-08-13T03:04:22.467010Z","submitted_at":"2025-06-18T20:01:14Z","title":"Cross-Modality Learning for Predicting IHC Biomarkers from H&E-Stained Whole-Slide Images","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T23:53:46.847735Z"},"links":{"citing_paper":"/paper/2506.15853"},"observation_digest":"sha256:3ec6ad15253240789b93e019ff0a586b564e398ef923107d6540c7ff4c285e3a","observation_id":"7f5d39f0-6405-42bc-8f78-d40407cedc3e","resolution":{"observed_at":"2026-08-06T23:53:50.875706Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:53:50.669092Z","title":null,"venue":null,"work_id":"28a82695-5e7c-4c01-92e4-df5086b3ab94","year":2020},"citing_paper":{"arxiv_id":"2506.15853","last_updated":"2025-06-18T20:01:14Z","snapshot_observed_at":"2026-08-13T03:04:22.467010Z","submitted_at":"2025-06-18T20:01:14Z","title":"Cross-Modality Learning for Predicting IHC Biomarkers from H&E-Stained Whole-Slide Images","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T23:53:46.967079Z"},"links":{"citing_paper":"/paper/2506.15853"},"observation_digest":"sha256:d5259353b2b9f464a24ca2019675f54de43313eeaacf3fa0570321094e242e35","observation_id":"99c7ac67-b6ef-4020-a4f9-60307a466462","resolution":{"observed_at":"2026-08-06T23:53:50.766270Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2025.12056","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:53:48.783980Z","title":null,"venue":null,"work_id":"961cc02e-5e49-49e1-97d3-a26c696fc528","year":2025},"citing_paper":{"arxiv_id":"2506.15853","last_updated":"2025-06-18T20:01:14Z","snapshot_observed_at":"2026-08-13T03:04:22.467010Z","submitted_at":"2025-06-18T20:01:14Z","title":"Cross-Modality Learning for Predicting IHC Biomarkers from H&E-Stained Whole-Slide Images","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T23:53:47.155654Z"},"links":{"citing_paper":"/paper/2506.15853"},"observation_digest":"sha256:fd4c725d7522ad8b6d1faa9d590d29d6b7bd0a000555bfa30e5e6f0ecbb09604","observation_id":"f9598e95-67e0-49ed-bb0d-27ca53db1a15","resolution":{"observed_at":"2026-08-06T23:53:48.919647Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:53:50.435598Z","title":null,"venue":null,"work_id":"f923652c-f85c-47ff-90b5-0916cf006c55","year":2022},"citing_paper":{"arxiv_id":"2506.15853","last_updated":"2025-06-18T20:01:14Z","snapshot_observed_at":"2026-08-13T03:04:22.467010Z","submitted_at":"2025-06-18T20:01:14Z","title":"Cross-Modality Learning for Predicting IHC Biomarkers from H&E-Stained Whole-Slide Images","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T23:53:47.288674Z"},"links":{"citing_paper":"/paper/2506.15853"},"observation_digest":"sha256:a89b4f49611aa715e205bdcfdebfcc9b7ac156ce4a6e9633e05b8ce4bcc390aa","observation_id":"1a2e1905-4322-487c-8d91-d11647045e45","resolution":{"observed_at":"2026-08-06T23:53:50.501570Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:53:50.181364Z","title":null,"venue":null,"work_id":"09664583-7b65-4e06-9848-f0a35e05b865","year":2022},"citing_paper":{"arxiv_id":"2506.15853","last_updated":"2025-06-18T20:01:14Z","snapshot_observed_at":"2026-08-13T03:04:22.467010Z","submitted_at":"2025-06-18T20:01:14Z","title":"Cross-Modality Learning for Predicting IHC Biomarkers from H&E-Stained Whole-Slide Images","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T23:53:47.436493Z"},"links":{"citing_paper":"/paper/2506.15853"},"observation_digest":"sha256:4a93ef05e36673378e96ffc8d64183462912c617d3632e982e15df33a61b031a","observation_id":"53ff5cec-d6e1-4cda-8acd-449fd5285a72","resolution":{"observed_at":"2026-08-06T23:53:50.318263Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:53:49.935221Z","title":null,"venue":null,"work_id":"de7a14f8-7615-475a-9d5a-5d2a4fbe6d99","year":2023},"citing_paper":{"arxiv_id":"2506.15853","last_updated":"2025-06-18T20:01:14Z","snapshot_observed_at":"2026-08-13T03:04:22.467010Z","submitted_at":"2025-06-18T20:01:14Z","title":"Cross-Modality Learning for Predicting IHC Biomarkers from H&E-Stained Whole-Slide Images","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T23:53:47.549623Z"},"links":{"citing_paper":"/paper/2506.15853"},"observation_digest":"sha256:922872f7fc5512a55be396e17a47c8269749ae6034e054f5c29c2c130a127d74","observation_id":"cc4e734d-dd41-4a5a-8e99-aeffb565453e","resolution":{"observed_at":"2026-08-06T23:53:50.033881Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:53:49.675175Z","title":null,"venue":null,"work_id":"02b9f137-cc33-476a-ba68-e4645fd7d587","year":2024},"citing_paper":{"arxiv_id":"2506.15853","last_updated":"2025-06-18T20:01:14Z","snapshot_observed_at":"2026-08-13T03:04:22.467010Z","submitted_at":"2025-06-18T20:01:14Z","title":"Cross-Modality Learning for Predicting IHC Biomarkers from H&E-Stained Whole-Slide Images","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T23:53:47.636232Z"},"links":{"citing_paper":"/paper/2506.15853"},"observation_digest":"sha256:4d6acf6e7bc56a35d5af968867eca86c85f9b6ecfef6bf9bb2f802dbdfb5fdb3","observation_id":"709946a8-24df-4296-92f6-c73ea7d43196","resolution":{"observed_at":"2026-08-06T23:53:49.784285Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.11618","last_updated":"2024-05-19T17:17:35Z","snapshot_observed_at":"2026-08-13T00:04:15.463690Z","submitted_at":"2024-05-19T17:17:35Z","title":"Transcriptomics-guided Slide Representation Learning in Computational Pathology","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.11618","snapshot_observed_at":"2026-08-06T23:53:47.742570Z","title":"J., Williamson, D","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.15853","last_updated":"2025-06-18T20:01:14Z","snapshot_observed_at":"2026-08-13T03:04:22.467010Z","submitted_at":"2025-06-18T20:01:14Z","title":"Cross-Modality Learning for Predicting IHC Biomarkers from H&E-Stained Whole-Slide Images","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T23:53:47.742570Z"},"links":{"cited_paper":"/paper/2405.11618","citing_paper":"/paper/2506.15853"},"observation_digest":"sha256:a253880eecacc450da78d711585b1eabf17c85887ba29865477acc7a57fdf974","observation_id":"2be2a019-dea2-409a-8b72-65950fd3c7c3","resolution":{"observed_at":"2026-08-06T23:53:47.742570Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:53:49.428016Z","title":"Y., Chen, B., Williamson, D","venue":null,"work_id":"0f60cb4b-f5b4-43dd-ae65-6b22bc21865d","year":2024},"citing_paper":{"arxiv_id":"2506.15853","last_updated":"2025-06-18T20:01:14Z","snapshot_observed_at":"2026-08-13T03:04:22.467010Z","submitted_at":"2025-06-18T20:01:14Z","title":"Cross-Modality Learning for Predicting IHC Biomarkers from H&E-Stained Whole-Slide Images","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T23:53:47.860327Z"},"links":{"citing_paper":"/paper/2506.15853"},"observation_digest":"sha256:7a7e17508372810b394ece6dd2dd3f80c95bb5b771b5bbb9e884dbaaa6c5bf4e","observation_id":"aee18ea7-2016-44e9-bc71-c6a9c3c5f413","resolution":{"observed_at":"2026-08-06T23:53:49.548894Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:53:49.158326Z","title":"J., Ding, T., Lu, M","venue":null,"work_id":"93c28321-5d22-47d8-af2a-2c6553d8944a","year":2024},"citing_paper":{"arxiv_id":"2506.15853","last_updated":"2025-06-18T20:01:14Z","snapshot_observed_at":"2026-08-13T03:04:22.467010Z","submitted_at":"2025-06-18T20:01:14Z","title":"Cross-Modality Learning for Predicting IHC Biomarkers from H&E-Stained Whole-Slide Images","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T23:53:47.983427Z"},"links":{"citing_paper":"/paper/2506.15853"},"observation_digest":"sha256:fc6e9ea7a3524642aba5e783a24c2d3614e79c63b1e97c13639606c72188552a","observation_id":"5365631f-d7ff-477b-8881-f19ee050fcf7","resolution":{"observed_at":"2026-08-06T23:53:49.293303Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:53:48.980294Z","title":null,"venue":null,"work_id":"58156859-4a81-4bad-878b-e893112fa830","year":2021},"citing_paper":{"arxiv_id":"2506.15853","last_updated":"2025-06-18T20:01:14Z","snapshot_observed_at":"2026-08-13T03:04:22.467010Z","submitted_at":"2025-06-18T20:01:14Z","title":"Cross-Modality Learning for Predicting IHC Biomarkers from H&E-Stained Whole-Slide Images","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T23:53:48.155699Z"},"links":{"citing_paper":"/paper/2506.15853"},"observation_digest":"sha256:451f7a1eed7eefe4591d0436f118bfa6170beb62b977119b2541b5d7f4a7e951","observation_id":"5aa1b014-7492-4f50-95e9-bc556ba35bd5","resolution":{"observed_at":"2026-08-06T23:53:49.047488Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.15853","last_updated":"2025-06-18T20:01:14Z","latest_version":1,"primary_category":"eess.IV","snapshot_observed_at":"2026-08-13T03:04:22.467010Z","submitted_at":"2025-06-18T20:01:14Z","title":"Cross-Modality Learning for Predicting IHC Biomarkers from H&E-Stained Whole-Slide Images"},"reference_resolution":{"displayed":28,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":14,"verified_exact":2,"verified_fuzzy":12},"total_outbound_references":28},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2506.15853."}