{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:WLLNIAUGUMG67OSWS7MVZLLSUJ","short_pith_number":"pith:WLLNIAUG","canonical_record":{"source":{"id":"1807.00431","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-02T01:57:38Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"009c90fa216b720460b0743dd32c5a50680ffd34a3e5e2a00b96ffb7a70a3e79","abstract_canon_sha256":"eb2ca7a00a143debde6f08f23fc32a770e84f1cdccb2d2ea0426156cfd822e8a"},"schema_version":"1.0"},"canonical_sha256":"b2d6d40286a30defba5697d95cad72a25473f844500e4943b18f9bdb29ed3f7e","source":{"kind":"arxiv","id":"1807.00431","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.00431","created_at":"2026-05-17T23:52:15Z"},{"alias_kind":"arxiv_version","alias_value":"1807.00431v2","created_at":"2026-05-17T23:52:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.00431","created_at":"2026-05-17T23:52:15Z"},{"alias_kind":"pith_short_12","alias_value":"WLLNIAUGUMG6","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_16","alias_value":"WLLNIAUGUMG67OSW","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_8","alias_value":"WLLNIAUG","created_at":"2026-05-18T12:33:01Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:WLLNIAUGUMG67OSWS7MVZLLSUJ","target":"record","payload":{"canonical_record":{"source":{"id":"1807.00431","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-02T01:57:38Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"009c90fa216b720460b0743dd32c5a50680ffd34a3e5e2a00b96ffb7a70a3e79","abstract_canon_sha256":"eb2ca7a00a143debde6f08f23fc32a770e84f1cdccb2d2ea0426156cfd822e8a"},"schema_version":"1.0"},"canonical_sha256":"b2d6d40286a30defba5697d95cad72a25473f844500e4943b18f9bdb29ed3f7e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:52:15.962465Z","signature_b64":"Ci9gi652cTvAe+j82G0ngKeRpmX7p6Lo7oOLMftnRSTRBRhutYwgVMjYgK8HMIMkEy2QEgWzEdPTGb1cEJdVDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b2d6d40286a30defba5697d95cad72a25473f844500e4943b18f9bdb29ed3f7e","last_reissued_at":"2026-05-17T23:52:15.961795Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:52:15.961795Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1807.00431","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:52:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZNCiXv2xB9rASSbwEjyQYz7UDhQhCzrYPOXS/FENNn/gbsgNuSI2ADFzwzihHhxhp4oPx43mOX2xdylv9E/ECg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-28T15:54:33.026402Z"},"content_sha256":"d05b7a4386fc18d43b0aab3f200fdafde57be6dca2e7888069fe75910892a92c","schema_version":"1.0","event_id":"sha256:d05b7a4386fc18d43b0aab3f200fdafde57be6dca2e7888069fe75910892a92c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:WLLNIAUGUMG67OSWS7MVZLLSUJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Confounding variables can degrade generalization performance of radiological deep learning models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"Anthony B. Costa, Eric K. Oermann, John R. Zech, Joseph J. Titano, Manway Liu, Marcus A. Badgeley","submitted_at":"2018-07-02T01:57:38Z","abstract_excerpt":"Early results in using convolutional neural networks (CNNs) on x-rays to diagnose disease have been promising, but it has not yet been shown that models trained on x-rays from one hospital or one group of hospitals will work equally well at different hospitals. Before these tools are used for computer-aided diagnosis in real-world clinical settings, we must verify their ability to generalize across a variety of hospital systems. A cross-sectional design was used to train and evaluate pneumonia screening CNNs on 158,323 chest x-rays from NIH (n=112,120 from 30,805 patients), Mount Sinai (42,396"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.00431","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:52:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"H887OYYZ22g0Qtjb8MtiRzCHKGzPkHjHejquVfcCM6LywvgrEvq70cPyk6q7z4ue+FFjx2MribKsiaVsIpoWCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-28T15:54:33.026739Z"},"content_sha256":"1ffdb2cbee946a0675319056ff71c58be480167196619fe87edccd6a7822672d","schema_version":"1.0","event_id":"sha256:1ffdb2cbee946a0675319056ff71c58be480167196619fe87edccd6a7822672d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WLLNIAUGUMG67OSWS7MVZLLSUJ/bundle.json","state_url":"https://pith.science/pith/WLLNIAUGUMG67OSWS7MVZLLSUJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WLLNIAUGUMG67OSWS7MVZLLSUJ/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-28T15:54:33Z","links":{"resolver":"https://pith.science/pith/WLLNIAUGUMG67OSWS7MVZLLSUJ","bundle":"https://pith.science/pith/WLLNIAUGUMG67OSWS7MVZLLSUJ/bundle.json","state":"https://pith.science/pith/WLLNIAUGUMG67OSWS7MVZLLSUJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WLLNIAUGUMG67OSWS7MVZLLSUJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:WLLNIAUGUMG67OSWS7MVZLLSUJ","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"eb2ca7a00a143debde6f08f23fc32a770e84f1cdccb2d2ea0426156cfd822e8a","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-02T01:57:38Z","title_canon_sha256":"009c90fa216b720460b0743dd32c5a50680ffd34a3e5e2a00b96ffb7a70a3e79"},"schema_version":"1.0","source":{"id":"1807.00431","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.00431","created_at":"2026-05-17T23:52:15Z"},{"alias_kind":"arxiv_version","alias_value":"1807.00431v2","created_at":"2026-05-17T23:52:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.00431","created_at":"2026-05-17T23:52:15Z"},{"alias_kind":"pith_short_12","alias_value":"WLLNIAUGUMG6","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_16","alias_value":"WLLNIAUGUMG67OSW","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_8","alias_value":"WLLNIAUG","created_at":"2026-05-18T12:33:01Z"}],"graph_snapshots":[{"event_id":"sha256:1ffdb2cbee946a0675319056ff71c58be480167196619fe87edccd6a7822672d","target":"graph","created_at":"2026-05-17T23:52:15Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Early results in using convolutional neural networks (CNNs) on x-rays to diagnose disease have been promising, but it has not yet been shown that models trained on x-rays from one hospital or one group of hospitals will work equally well at different hospitals. Before these tools are used for computer-aided diagnosis in real-world clinical settings, we must verify their ability to generalize across a variety of hospital systems. A cross-sectional design was used to train and evaluate pneumonia screening CNNs on 158,323 chest x-rays from NIH (n=112,120 from 30,805 patients), Mount Sinai (42,396","authors_text":"Anthony B. Costa, Eric K. Oermann, John R. Zech, Joseph J. Titano, Manway Liu, Marcus A. Badgeley","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-02T01:57:38Z","title":"Confounding variables can degrade generalization performance of radiological deep learning models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.00431","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:d05b7a4386fc18d43b0aab3f200fdafde57be6dca2e7888069fe75910892a92c","target":"record","created_at":"2026-05-17T23:52:15Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"eb2ca7a00a143debde6f08f23fc32a770e84f1cdccb2d2ea0426156cfd822e8a","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-02T01:57:38Z","title_canon_sha256":"009c90fa216b720460b0743dd32c5a50680ffd34a3e5e2a00b96ffb7a70a3e79"},"schema_version":"1.0","source":{"id":"1807.00431","kind":"arxiv","version":2}},"canonical_sha256":"b2d6d40286a30defba5697d95cad72a25473f844500e4943b18f9bdb29ed3f7e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b2d6d40286a30defba5697d95cad72a25473f844500e4943b18f9bdb29ed3f7e","first_computed_at":"2026-05-17T23:52:15.961795Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:52:15.961795Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Ci9gi652cTvAe+j82G0ngKeRpmX7p6Lo7oOLMftnRSTRBRhutYwgVMjYgK8HMIMkEy2QEgWzEdPTGb1cEJdVDA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:52:15.962465Z","signed_message":"canonical_sha256_bytes"},"source_id":"1807.00431","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d05b7a4386fc18d43b0aab3f200fdafde57be6dca2e7888069fe75910892a92c","sha256:1ffdb2cbee946a0675319056ff71c58be480167196619fe87edccd6a7822672d"],"state_sha256":"0eff9da1028f5e92782aff451c5eece21f1b25fc2fef1832d7b8fa4864f8b8b3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oH6BMhPeb0+t9oeGp06F9/8uos7+o982SAZOcdG89/8MF3oaEG1a6YZ3qhLL5urc7ZBTvAnmNr/C06xLMxgRCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-28T15:54:33.028545Z","bundle_sha256":"928e855f06fcf038a4f291b8a3d0b90f37474b3327b60ffcbcf37e49545df2af"}}