{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:POCEKUBM642AGPB4FPBH3XGNNS","short_pith_number":"pith:POCEKUBM","schema_version":"1.0","canonical_sha256":"7b8445502cf734033c3c2bc27ddccd6c8d37b516017c9868f82bd26c79ddc84e","source":{"kind":"arxiv","id":"2508.14821","version":1},"attestation_state":"computed","paper":{"title":"The C-index Multiverse","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","stat.AP"],"primary_cat":"stat.ML","authors_text":"Bego\\~na B. Sierra, Catalina A. Vallejos, Colin McLean, Peter S. Hall","submitted_at":"2025-08-20T16:11:10Z","abstract_excerpt":"Quantifying out-of-sample discrimination performance for time-to-event outcomes is a fundamental step for model evaluation and selection in the context of predictive modelling. The concordance index, or C-index, is a widely used metric for this purpose, particularly with the growing development of machine learning methods. Beyond differences between proposed C-index estimators (e.g. Harrell's, Uno's and Antolini's), we demonstrate the existence of a C-index multiverse among available R and python software, where seemingly equal implementations can yield different results. This can undermine re"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2508.14821","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-08-20T16:11:10Z","cross_cats_sorted":["cs.LG","stat.AP"],"title_canon_sha256":"1f03c96d88465454b50d2e53d38eb82971b6b43079e8d8c38de31b8dfe13dbd5","abstract_canon_sha256":"bd1b444fb7911d4f6285325b58a8545023cb4e9dbfb0971460ec63d0c19efe49"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:56:45.140420Z","signature_b64":"bNAHUb3zQHJApWfcKpocUlxnBSr+P3CQd/bpzup4rtqxIX7DecKCwxntoJg8p/dE4L2YDxcLK9PuHkSDfuxnDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7b8445502cf734033c3c2bc27ddccd6c8d37b516017c9868f82bd26c79ddc84e","last_reissued_at":"2026-07-05T11:56:45.139984Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:56:45.139984Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The C-index Multiverse","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","stat.AP"],"primary_cat":"stat.ML","authors_text":"Bego\\~na B. Sierra, Catalina A. Vallejos, Colin McLean, Peter S. Hall","submitted_at":"2025-08-20T16:11:10Z","abstract_excerpt":"Quantifying out-of-sample discrimination performance for time-to-event outcomes is a fundamental step for model evaluation and selection in the context of predictive modelling. The concordance index, or C-index, is a widely used metric for this purpose, particularly with the growing development of machine learning methods. Beyond differences between proposed C-index estimators (e.g. Harrell's, Uno's and Antolini's), we demonstrate the existence of a C-index multiverse among available R and python software, where seemingly equal implementations can yield different results. This can undermine re"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.14821","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2508.14821/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2508.14821","created_at":"2026-07-05T11:56:45.140039+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.14821v1","created_at":"2026-07-05T11:56:45.140039+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.14821","created_at":"2026-07-05T11:56:45.140039+00:00"},{"alias_kind":"pith_short_12","alias_value":"POCEKUBM642A","created_at":"2026-07-05T11:56:45.140039+00:00"},{"alias_kind":"pith_short_16","alias_value":"POCEKUBM642AGPB4","created_at":"2026-07-05T11:56:45.140039+00:00"},{"alias_kind":"pith_short_8","alias_value":"POCEKUBM","created_at":"2026-07-05T11:56:45.140039+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.24678","citing_title":"Landmarking with Latent Class Mixed Models for Dynamic Prediction of Time-to-event Data with Heterogeneous Biomarker Trajectories","ref_index":54,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/POCEKUBM642AGPB4FPBH3XGNNS","json":"https://pith.science/pith/POCEKUBM642AGPB4FPBH3XGNNS.json","graph_json":"https://pith.science/api/pith-number/POCEKUBM642AGPB4FPBH3XGNNS/graph.json","events_json":"https://pith.science/api/pith-number/POCEKUBM642AGPB4FPBH3XGNNS/events.json","paper":"https://pith.science/paper/POCEKUBM"},"agent_actions":{"view_html":"https://pith.science/pith/POCEKUBM642AGPB4FPBH3XGNNS","download_json":"https://pith.science/pith/POCEKUBM642AGPB4FPBH3XGNNS.json","view_paper":"https://pith.science/paper/POCEKUBM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.14821&json=true","fetch_graph":"https://pith.science/api/pith-number/POCEKUBM642AGPB4FPBH3XGNNS/graph.json","fetch_events":"https://pith.science/api/pith-number/POCEKUBM642AGPB4FPBH3XGNNS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/POCEKUBM642AGPB4FPBH3XGNNS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/POCEKUBM642AGPB4FPBH3XGNNS/action/storage_attestation","attest_author":"https://pith.science/pith/POCEKUBM642AGPB4FPBH3XGNNS/action/author_attestation","sign_citation":"https://pith.science/pith/POCEKUBM642AGPB4FPBH3XGNNS/action/citation_signature","submit_replication":"https://pith.science/pith/POCEKUBM642AGPB4FPBH3XGNNS/action/replication_record"}},"created_at":"2026-07-05T11:56:45.140039+00:00","updated_at":"2026-07-05T11:56:45.140039+00:00"}