{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:VGHOFPKR2N6TO2IFTVW4OHO3RG","short_pith_number":"pith:VGHOFPKR","schema_version":"1.0","canonical_sha256":"a98ee2bd51d37d3769059d6dc71ddb89b46686dd49b6d9d7c8d6ad116ae2a05d","source":{"kind":"arxiv","id":"2212.12930","version":1},"attestation_state":"computed","paper":{"title":"Modeling restricted enrollment and optimal cost-efficient design in multicenter clinical trials","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Matthew Austin, Vladimir Anisimov","submitted_at":"2022-12-25T16:43:38Z","abstract_excerpt":"Design and forecasting of patient enrollment is among the greatest challenges that the clinical research enterprize faces today, as inefficient enrollment can be a major cause of drug development delays. Therefore, the development of the innovative statistical and artificial intelligence technologies for improving the efficiency of clinical trials operation are of the imperative need. This paper is describing further developments in the innovative statistical methodology for modeling and forecasting patient enrollment. The underlying technique uses a Poisson-gamma enrollment model developed by"},"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":"2212.12930","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2022-12-25T16:43:38Z","cross_cats_sorted":[],"title_canon_sha256":"9d28a88125695a0ad51b1d3c2b1d2922755a377de77e4886da46169e37dd7a91","abstract_canon_sha256":"dffaf7246bf55d322d3ff93b80115e3d55ef5e29e03a939892e8bfff58188c61"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:28:14.835215Z","signature_b64":"+xM6fiv6JeM33BuxKWiiuucHPzwBu+2s3Bio2Cp2K6RUXhykfuqsSv04lUZx3uyZQS+w0elrulObJgINbrV/Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a98ee2bd51d37d3769059d6dc71ddb89b46686dd49b6d9d7c8d6ad116ae2a05d","last_reissued_at":"2026-07-05T05:28:14.834772Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:28:14.834772Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Modeling restricted enrollment and optimal cost-efficient design in multicenter clinical trials","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Matthew Austin, Vladimir Anisimov","submitted_at":"2022-12-25T16:43:38Z","abstract_excerpt":"Design and forecasting of patient enrollment is among the greatest challenges that the clinical research enterprize faces today, as inefficient enrollment can be a major cause of drug development delays. Therefore, the development of the innovative statistical and artificial intelligence technologies for improving the efficiency of clinical trials operation are of the imperative need. This paper is describing further developments in the innovative statistical methodology for modeling and forecasting patient enrollment. The underlying technique uses a Poisson-gamma enrollment model developed by"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.12930","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/2212.12930/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":"2212.12930","created_at":"2026-07-05T05:28:14.834827+00:00"},{"alias_kind":"arxiv_version","alias_value":"2212.12930v1","created_at":"2026-07-05T05:28:14.834827+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.12930","created_at":"2026-07-05T05:28:14.834827+00:00"},{"alias_kind":"pith_short_12","alias_value":"VGHOFPKR2N6T","created_at":"2026-07-05T05:28:14.834827+00:00"},{"alias_kind":"pith_short_16","alias_value":"VGHOFPKR2N6TO2IF","created_at":"2026-07-05T05:28:14.834827+00:00"},{"alias_kind":"pith_short_8","alias_value":"VGHOFPKR","created_at":"2026-07-05T05:28:14.834827+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2411.17393","citing_title":"Patient recruitment forecasting in clinical trials using time-dependent Poisson-gamma model and homogeneity testing criteria","ref_index":10,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VGHOFPKR2N6TO2IFTVW4OHO3RG","json":"https://pith.science/pith/VGHOFPKR2N6TO2IFTVW4OHO3RG.json","graph_json":"https://pith.science/api/pith-number/VGHOFPKR2N6TO2IFTVW4OHO3RG/graph.json","events_json":"https://pith.science/api/pith-number/VGHOFPKR2N6TO2IFTVW4OHO3RG/events.json","paper":"https://pith.science/paper/VGHOFPKR"},"agent_actions":{"view_html":"https://pith.science/pith/VGHOFPKR2N6TO2IFTVW4OHO3RG","download_json":"https://pith.science/pith/VGHOFPKR2N6TO2IFTVW4OHO3RG.json","view_paper":"https://pith.science/paper/VGHOFPKR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2212.12930&json=true","fetch_graph":"https://pith.science/api/pith-number/VGHOFPKR2N6TO2IFTVW4OHO3RG/graph.json","fetch_events":"https://pith.science/api/pith-number/VGHOFPKR2N6TO2IFTVW4OHO3RG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VGHOFPKR2N6TO2IFTVW4OHO3RG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VGHOFPKR2N6TO2IFTVW4OHO3RG/action/storage_attestation","attest_author":"https://pith.science/pith/VGHOFPKR2N6TO2IFTVW4OHO3RG/action/author_attestation","sign_citation":"https://pith.science/pith/VGHOFPKR2N6TO2IFTVW4OHO3RG/action/citation_signature","submit_replication":"https://pith.science/pith/VGHOFPKR2N6TO2IFTVW4OHO3RG/action/replication_record"}},"created_at":"2026-07-05T05:28:14.834827+00:00","updated_at":"2026-07-05T05:28:14.834827+00:00"}