{"as_of":"2026-08-19T01:55:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0a60fd8a5ff56754e13397ee34ca500e1ca2e5592cfd8ec40df6d0bd979e24a1","coverage":[{"denominator":19,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":19,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T22:04:33.112647Z","state":"measured"},{"denominator":19,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":19,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+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/2505.08242/citation-record","integrity":"/paper/2505.08242/integrity","json":"/paper/2505.08242/citation-record.json","paper":"/paper/2505.08242"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T22:04:33.460548Z","title":"The heart sound dataset consists of 941 participants and 941 audio recordings, each approximately 20 seconds long, totaling over 5 hours in duration","venue":null,"work_id":"936217b8-4f8a-4739-965b-e7e005c96b7a","year":null},"citing_paper":{"arxiv_id":"2505.08242","last_updated":"2025-05-13T05:34:06Z","snapshot_observed_at":"2026-08-15T21:57:38.064132Z","submitted_at":"2025-05-13T05:34:06Z","title":"Congenital Heart Disease recognition using Deep Learning/Transformer models","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-15T22:04:33.043978Z"},"links":{"citing_paper":"/paper/2505.08242"},"observation_digest":"sha256:5ea823311fb7d451a0ad81594aa72a7a826b4e3f5fde3cde5c0aaa734108b115","observation_id":"f53a1c0f-065d-4588-8393-b04b0e2890f7","resolution":{"observed_at":"2026-08-15T22:04:33.464746Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"0120.7711","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T22:04:33.307995Z","title":"Originally the storage format of the files was DICOM","venue":null,"work_id":"dbcdc10a-70d8-4ae2-9170-6d99e9340546","year":2021},"citing_paper":{"arxiv_id":"2505.08242","last_updated":"2025-05-13T05:34:06Z","snapshot_observed_at":"2026-08-15T21:57:38.064132Z","submitted_at":"2025-05-13T05:34:06Z","title":"Congenital Heart Disease recognition using Deep Learning/Transformer models","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-15T22:04:33.048506Z"},"links":{"citing_paper":"/paper/2505.08242"},"observation_digest":"sha256:773f06b6e7d7b48632ea0a08bb3d94a50fe0e608b865759183754e0096fe3d3e","observation_id":"2330f7be-a900-49a3-93cc-ecfefac8bbb6","resolution":{"observed_at":"2026-08-15T22:04:33.313903Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1093/eurheartj/ehaa874","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T22:04:33.153583Z","title":"Long-term outcomes after myocardial infarction in middle-aged and older patients with congenital heart disease—a nationwide study,","venue":null,"work_id":"18ac805a-26ac-42e8-9d25-7c3c4079fd53","year":2020},"citing_paper":{"arxiv_id":"2505.08242","last_updated":"2025-05-13T05:34:06Z","snapshot_observed_at":"2026-08-15T21:57:38.064132Z","submitted_at":"2025-05-13T05:34:06Z","title":"Congenital Heart Disease recognition using Deep Learning/Transformer models","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-15T22:04:33.052890Z"},"links":{"citing_paper":"/paper/2505.08242"},"observation_digest":"sha256:2aab777786d606c56e010e28f3d4ad14a196b5f424ec79d416dff25c928104ba","observation_id":"453e6cf5-5049-4f1c-a7e1-6ba1809c2145","resolution":{"observed_at":"2026-08-15T22:04:33.157645Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T22:04:33.449769Z","title":"Diagnostic value of fetal echocardiography for congenital heart disease: A systematic review and meta-analysis,","venue":null,"work_id":"57236d42-3a29-4101-9f52-2da9240ab8dd","year":2015},"citing_paper":{"arxiv_id":"2505.08242","last_updated":"2025-05-13T05:34:06Z","snapshot_observed_at":"2026-08-15T21:57:38.064132Z","submitted_at":"2025-05-13T05:34:06Z","title":"Congenital Heart Disease recognition using Deep Learning/Transformer models","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-15T22:04:33.056782Z"},"links":{"citing_paper":"/paper/2505.08242"},"observation_digest":"sha256:d30b2cc6e1c7b7f0b0caca9567b0625a3d69380ac365330e3078591dc42548c1","observation_id":"eee863ea-2157-4bc4-a38d-a67ad73edaf1","resolution":{"observed_at":"2026-08-15T22:04:33.453384Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.04493","last_updated":"2025-01-08T13:26:24Z","snapshot_observed_at":"2026-08-16T16:02:22.009771Z","submitted_at":"2025-01-08T13:26:24Z","title":"The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights","version":1},"cited_work":{"arxiv_id":"2501.04493","doi":null,"metadata_source":"pith","pith_arxiv_id":"2501.04493","snapshot_observed_at":"2026-08-15T22:04:33.247177Z","title":"The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights","venue":"eess.IV","work_id":"02889f50-6fff-4737-b96b-118630717323","year":2025},"citing_paper":{"arxiv_id":"2505.08242","last_updated":"2025-05-13T05:34:06Z","snapshot_observed_at":"2026-08-15T21:57:38.064132Z","submitted_at":"2025-05-13T05:34:06Z","title":"Congenital Heart Disease recognition using Deep Learning/Transformer models","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-15T22:04:33.060613Z"},"links":{"cited_paper":"/paper/2501.04493","citing_paper":"/paper/2505.08242"},"observation_digest":"sha256:37e83051b29d0565422031023ec846acc97181cd0d7e9c265317e48672e2b4b3","observation_id":"582f68b0-1d43-48f4-981c-27ea1817c7aa","resolution":{"observed_at":"2026-08-15T22:04:33.250777Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T22:04:33.439153Z","title":"Detection and diagnosis of congenital heart disease from chest x-rays with deep learning models,","venue":null,"work_id":"b4bcf48b-df7d-4ac3-9f19-85ec9267e0f1","year":2025},"citing_paper":{"arxiv_id":"2505.08242","last_updated":"2025-05-13T05:34:06Z","snapshot_observed_at":"2026-08-15T21:57:38.064132Z","submitted_at":"2025-05-13T05:34:06Z","title":"Congenital Heart Disease recognition using Deep Learning/Transformer models","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-15T22:04:33.064901Z"},"links":{"citing_paper":"/paper/2505.08242"},"observation_digest":"sha256:9af1b2a759648f6120598b1c095861e91b32b2fe91f93703a7a6475adbf03d1e","observation_id":"88d218df-7a99-475a-acb9-f35060e8f9ee","resolution":{"observed_at":"2026-08-15T22:04:33.442684Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T22:04:33.429693Z","title":"Zchsound: Open-source zju paediatric heart sound database with congenital heart disease,","venue":null,"work_id":"1052aa19-b089-434d-800e-642c7d96edff","year":2024},"citing_paper":{"arxiv_id":"2505.08242","last_updated":"2025-05-13T05:34:06Z","snapshot_observed_at":"2026-08-15T21:57:38.064132Z","submitted_at":"2025-05-13T05:34:06Z","title":"Congenital Heart Disease recognition using Deep Learning/Transformer models","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T22:04:33.068674Z"},"links":{"citing_paper":"/paper/2505.08242"},"observation_digest":"sha256:3d2ab3e6022b67e3796b0b928327e9f1d2fa9512b21b00cd7020123d48188a2b","observation_id":"d3460174-de3e-4eb1-b454-63892e9f010e","resolution":{"observed_at":"2026-08-15T22:04:33.433001Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.3390/electronics12102221","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T22:04:33.139510Z","title":"Assisting Heart Valve Diseases Diagnosis via Transformer-Based Classification of Heart Sound Signals,","venue":null,"work_id":"8d91a4ae-bfbb-4611-8f86-6f21abe3ef46","year":2023},"citing_paper":{"arxiv_id":"2505.08242","last_updated":"2025-05-13T05:34:06Z","snapshot_observed_at":"2026-08-15T21:57:38.064132Z","submitted_at":"2025-05-13T05:34:06Z","title":"Congenital Heart Disease recognition using Deep Learning/Transformer models","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-15T22:04:33.072414Z"},"links":{"citing_paper":"/paper/2505.08242"},"observation_digest":"sha256:fd21df9ca22ce6ce734d832683277857a2a39104af34857f0728c2c35b13abec","observation_id":"454be57e-71ed-4f73-8a99-96f4f3097d83","resolution":{"observed_at":"2026-08-15T22:04:33.145703Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T22:04:33.420670Z","title":"Heart sounds classification with a fuzzy neural network method with structure learning,","venue":null,"work_id":"56a587f5-5d34-4b51-bf98-e9575b23f5a9","year":2012},"citing_paper":{"arxiv_id":"2505.08242","last_updated":"2025-05-13T05:34:06Z","snapshot_observed_at":"2026-08-15T21:57:38.064132Z","submitted_at":"2025-05-13T05:34:06Z","title":"Congenital Heart Disease recognition using Deep Learning/Transformer models","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T22:04:33.076002Z"},"links":{"citing_paper":"/paper/2505.08242"},"observation_digest":"sha256:bcc070140cc3da073655a3ed4a0f05051e0eeed973ddaf9dd528233da8f954af","observation_id":"d5b8db7c-4961-49c9-9622-4c962c8d2074","resolution":{"observed_at":"2026-08-15T22:04:33.423858Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T22:04:33.410894Z","title":"Heart sound classification based on scaled spectrogram and partial least squares regression,","venue":null,"work_id":"b59f7902-4b02-466d-b8a9-d29b3113d894","year":2017},"citing_paper":{"arxiv_id":"2505.08242","last_updated":"2025-05-13T05:34:06Z","snapshot_observed_at":"2026-08-15T21:57:38.064132Z","submitted_at":"2025-05-13T05:34:06Z","title":"Congenital Heart Disease recognition using Deep Learning/Transformer models","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T22:04:33.079406Z"},"links":{"citing_paper":"/paper/2505.08242"},"observation_digest":"sha256:c7cfff1cccd46fb5daaa8c8cc1ba490ccb371a19614198fc5276a0dd0d651fca","observation_id":"31c34b60-2151-41a9-9e06-97779961f574","resolution":{"observed_at":"2026-08-15T22:04:33.414520Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T22:04:33.399570Z","title":"Classification of heart sound signal using curve fitting and fractal dimension,","venue":null,"work_id":"4252d30c-dc3b-49e7-8a14-e6a17be7acc7","year":2018},"citing_paper":{"arxiv_id":"2505.08242","last_updated":"2025-05-13T05:34:06Z","snapshot_observed_at":"2026-08-15T21:57:38.064132Z","submitted_at":"2025-05-13T05:34:06Z","title":"Congenital Heart Disease recognition using Deep Learning/Transformer models","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T22:04:33.082862Z"},"links":{"citing_paper":"/paper/2505.08242"},"observation_digest":"sha256:2c3486e054b7a79d2ccbee781c5df63a2fbc6c20063419891f08b9409ced195c","observation_id":"546ef40e-a1df-4a98-baa8-74b77936862f","resolution":{"observed_at":"2026-08-15T22:04:33.403514Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2024.13519","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T22:04:33.228337Z","title":"CHD- CXR: A De-identified Publicly Available Dataset of Chest X- ray for Congenital Heart Disease,","venue":null,"work_id":"d3647c5d-a5cf-40eb-b7b0-f3a571bdd961","year":2024},"citing_paper":{"arxiv_id":"2505.08242","last_updated":"2025-05-13T05:34:06Z","snapshot_observed_at":"2026-08-15T21:57:38.064132Z","submitted_at":"2025-05-13T05:34:06Z","title":"Congenital Heart Disease recognition using Deep Learning/Transformer models","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-15T22:04:33.086388Z"},"links":{"citing_paper":"/paper/2505.08242"},"observation_digest":"sha256:e5b20545fab2b48faccef57b710d5fd81f1c52c3a002595af3c4a762f36679ff","observation_id":"8cd4b0c8-1727-47b5-9bb0-515746744e3a","resolution":{"observed_at":"2026-08-15T22:04:33.235646Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T22:04:33.388669Z","title":"Jiang, J","venue":null,"work_id":"80259e51-a8c8-443d-856f-afe969971835","year":2023},"citing_paper":{"arxiv_id":"2505.08242","last_updated":"2025-05-13T05:34:06Z","snapshot_observed_at":"2026-08-15T21:57:38.064132Z","submitted_at":"2025-05-13T05:34:06Z","title":"Congenital Heart Disease recognition using Deep Learning/Transformer models","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-15T22:04:33.090093Z"},"links":{"citing_paper":"/paper/2505.08242"},"observation_digest":"sha256:f94c44b0f8f1372def4361632ebede1c8e618e7e0c65ec13fc0bc63f7c2f2675","observation_id":"2d038524-46aa-4920-8ee9-ea7b68967a72","resolution":{"observed_at":"2026-08-15T22:04:33.392268Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T22:04:33.377714Z","title":"Genetic contribution to congenital heart disease (chd),","venue":null,"work_id":"0310dbaa-c1d5-4746-abdc-fcbeee0d39ae","year":2020},"citing_paper":{"arxiv_id":"2505.08242","last_updated":"2025-05-13T05:34:06Z","snapshot_observed_at":"2026-08-15T21:57:38.064132Z","submitted_at":"2025-05-13T05:34:06Z","title":"Congenital Heart Disease recognition using Deep Learning/Transformer models","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-15T22:04:33.093814Z"},"links":{"citing_paper":"/paper/2505.08242"},"observation_digest":"sha256:4aa66f9838c9909df29af93c5775d3ff8074e22994005b431c429595d8cf840f","observation_id":"49ad6ae7-ce48-47f7-8181-007af6bb86cd","resolution":{"observed_at":"2026-08-15T22:04:33.381500Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T22:04:33.367038Z","title":"Theory of edge detection,","venue":null,"work_id":"a8cf5dba-1794-4c8a-b345-9dc69c36cf3d","year":1980},"citing_paper":{"arxiv_id":"2505.08242","last_updated":"2025-05-13T05:34:06Z","snapshot_observed_at":"2026-08-15T21:57:38.064132Z","submitted_at":"2025-05-13T05:34:06Z","title":"Congenital Heart Disease recognition using Deep Learning/Transformer models","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-15T22:04:33.097503Z"},"links":{"citing_paper":"/paper/2505.08242"},"observation_digest":"sha256:163274ecb12f4adf4eb42502d67015b15548c2c372b8162429b09e997f1d3af1","observation_id":"19129712-f50a-41de-bf5c-4c173ef77dfe","resolution":{"observed_at":"2026-08-15T22:04:33.370571Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T22:04:33.356334Z","title":"Gaussian blurring technique for detecting and classifying acute lymphoblastic leukemia cancer cells from microscopic biopsy images,","venue":null,"work_id":"4fd85732-c9c5-4ed4-826f-4e1639a46043","year":2023},"citing_paper":{"arxiv_id":"2505.08242","last_updated":"2025-05-13T05:34:06Z","snapshot_observed_at":"2026-08-15T21:57:38.064132Z","submitted_at":"2025-05-13T05:34:06Z","title":"Congenital Heart Disease recognition using Deep Learning/Transformer models","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-15T22:04:33.101589Z"},"links":{"citing_paper":"/paper/2505.08242"},"observation_digest":"sha256:d35e6344a14f9df99eb585bc6de2ab35e7396c42bb8da8e96730490f5213d56e","observation_id":"c2985067-b14e-42e7-b4f2-9f1b3322a9dc","resolution":{"observed_at":"2026-08-15T22:04:33.360332Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T22:04:33.345310Z","title":"Can ai help in screening viral and covid-19 pneumonia?","venue":null,"work_id":"478cc2e5-0ac6-48cf-b7ff-2aaca0300f80","year":2021},"citing_paper":{"arxiv_id":"2505.08242","last_updated":"2025-05-13T05:34:06Z","snapshot_observed_at":"2026-08-15T21:57:38.064132Z","submitted_at":"2025-05-13T05:34:06Z","title":"Congenital Heart Disease recognition using Deep Learning/Transformer models","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-15T22:04:33.105456Z"},"links":{"citing_paper":"/paper/2505.08242"},"observation_digest":"sha256:47807686f00b1b05b88a7be627c1da0ae766c969535f8fc2625bca6b8585e087","observation_id":"48b3f72a-b57a-4af6-b001-11978e52f0b2","resolution":{"observed_at":"2026-08-15T22:04:33.349227Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T22:04:33.334123Z","title":"Automated abnormality classification of chest radiographs using deep convolutional neural networks,","venue":null,"work_id":"1b92b95b-549b-4fa3-986b-e0b998eecd97","year":2020},"citing_paper":{"arxiv_id":"2505.08242","last_updated":"2025-05-13T05:34:06Z","snapshot_observed_at":"2026-08-15T21:57:38.064132Z","submitted_at":"2025-05-13T05:34:06Z","title":"Congenital Heart Disease recognition using Deep Learning/Transformer models","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-15T22:04:33.109091Z"},"links":{"citing_paper":"/paper/2505.08242"},"observation_digest":"sha256:6c5f08bec3fe3006b0d9225bb48127c77ac750668bc57874b5bbddff972b764d","observation_id":"f0e90baa-0465-40f0-a3ba-e4270fe7bf56","resolution":{"observed_at":"2026-08-15T22:04:33.338306Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-15T22:04:33.322864Z","title":"Efficient deep network architectures for fast chest x-ray tuberculosis screening and visualization,","venue":null,"work_id":"a9e1315e-2fd3-4891-90e1-b2420aa2e194","year":2021},"citing_paper":{"arxiv_id":"2505.08242","last_updated":"2025-05-13T05:34:06Z","snapshot_observed_at":"2026-08-15T21:57:38.064132Z","submitted_at":"2025-05-13T05:34:06Z","title":"Congenital Heart Disease recognition using Deep Learning/Transformer models","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-15T22:04:33.112647Z"},"links":{"citing_paper":"/paper/2505.08242"},"observation_digest":"sha256:316b1681ed66b1a2320f0209bf0642a00e3866704b0d827235a3403259c5d4bc","observation_id":"279278e1-d0e2-499c-a523-5c96bf3a0897","resolution":{"observed_at":"2026-08-15T22:04:33.326893Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2505.08242","last_updated":"2025-05-13T05:34:06Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-15T21:57:38.064132Z","submitted_at":"2025-05-13T05:34:06Z","title":"Congenital Heart Disease recognition using Deep Learning/Transformer models"},"reference_resolution":{"displayed":19,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":0,"verified_exact":4,"verified_fuzzy":14},"total_outbound_references":19},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2505.08242."}