{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:RCY7PAXK36EWYNPDUJQ4YBC4CR","short_pith_number":"pith:RCY7PAXK","schema_version":"1.0","canonical_sha256":"88b1f782eadf896c35e3a261cc045c146e66f6f00b26913bba0bcaa10da206ec","source":{"kind":"arxiv","id":"2106.13869","version":2},"attestation_state":"computed","paper":{"title":"A multi-stage machine learning model on diagnosis of esophageal manometry","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Alexandra J. Baumann, Dustin A. Carlson, Erica N. Donnan, Jacob M. Schauer, John E. Pandolfino, Mozziyar Etemadi, Wenjun Kou","submitted_at":"2021-06-25T20:09:23Z","abstract_excerpt":"High-resolution manometry (HRM) is the primary procedure used to diagnose esophageal motility disorders. Its interpretation and classification includes an initial evaluation of swallow-level outcomes and then derivation of a study-level diagnosis based on Chicago Classification (CC), using a tree-like algorithm. This diagnostic approach on motility disordered using HRM was mirrored using a multi-stage modeling framework developed using a combination of various machine learning approaches. Specifically, the framework includes deep-learning models at the swallow-level stage and feature-based mac"},"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":"2106.13869","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2021-06-25T20:09:23Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"4e08e8f216ce0c4fe4fd2fd26df77b6b49a1c1d0669151c6e048485f4eb801e6","abstract_canon_sha256":"5a19cd6ebd3f3a6d3561c56a359f5418baf63e70f11ea2b4ff0874d6240accf2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:25:42.823003Z","signature_b64":"WbwHJhbkhw0GplJ/OUJp5EupC66FyrAS5cnNGEjxPZJPGR/XRBul1DR6gtHrR/xoXo0BW6rJ/VLXDO0RRVuDAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"88b1f782eadf896c35e3a261cc045c146e66f6f00b26913bba0bcaa10da206ec","last_reissued_at":"2026-07-05T04:25:42.822536Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:25:42.822536Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A multi-stage machine learning model on diagnosis of esophageal manometry","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Alexandra J. Baumann, Dustin A. Carlson, Erica N. Donnan, Jacob M. Schauer, John E. Pandolfino, Mozziyar Etemadi, Wenjun Kou","submitted_at":"2021-06-25T20:09:23Z","abstract_excerpt":"High-resolution manometry (HRM) is the primary procedure used to diagnose esophageal motility disorders. Its interpretation and classification includes an initial evaluation of swallow-level outcomes and then derivation of a study-level diagnosis based on Chicago Classification (CC), using a tree-like algorithm. This diagnostic approach on motility disordered using HRM was mirrored using a multi-stage modeling framework developed using a combination of various machine learning approaches. Specifically, the framework includes deep-learning models at the swallow-level stage and feature-based mac"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.13869","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2106.13869/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":"2106.13869","created_at":"2026-07-05T04:25:42.822596+00:00"},{"alias_kind":"arxiv_version","alias_value":"2106.13869v2","created_at":"2026-07-05T04:25:42.822596+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.13869","created_at":"2026-07-05T04:25:42.822596+00:00"},{"alias_kind":"pith_short_12","alias_value":"RCY7PAXK36EW","created_at":"2026-07-05T04:25:42.822596+00:00"},{"alias_kind":"pith_short_16","alias_value":"RCY7PAXK36EWYNPD","created_at":"2026-07-05T04:25:42.822596+00:00"},{"alias_kind":"pith_short_8","alias_value":"RCY7PAXK","created_at":"2026-07-05T04:25:42.822596+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RCY7PAXK36EWYNPDUJQ4YBC4CR","json":"https://pith.science/pith/RCY7PAXK36EWYNPDUJQ4YBC4CR.json","graph_json":"https://pith.science/api/pith-number/RCY7PAXK36EWYNPDUJQ4YBC4CR/graph.json","events_json":"https://pith.science/api/pith-number/RCY7PAXK36EWYNPDUJQ4YBC4CR/events.json","paper":"https://pith.science/paper/RCY7PAXK"},"agent_actions":{"view_html":"https://pith.science/pith/RCY7PAXK36EWYNPDUJQ4YBC4CR","download_json":"https://pith.science/pith/RCY7PAXK36EWYNPDUJQ4YBC4CR.json","view_paper":"https://pith.science/paper/RCY7PAXK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2106.13869&json=true","fetch_graph":"https://pith.science/api/pith-number/RCY7PAXK36EWYNPDUJQ4YBC4CR/graph.json","fetch_events":"https://pith.science/api/pith-number/RCY7PAXK36EWYNPDUJQ4YBC4CR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RCY7PAXK36EWYNPDUJQ4YBC4CR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RCY7PAXK36EWYNPDUJQ4YBC4CR/action/storage_attestation","attest_author":"https://pith.science/pith/RCY7PAXK36EWYNPDUJQ4YBC4CR/action/author_attestation","sign_citation":"https://pith.science/pith/RCY7PAXK36EWYNPDUJQ4YBC4CR/action/citation_signature","submit_replication":"https://pith.science/pith/RCY7PAXK36EWYNPDUJQ4YBC4CR/action/replication_record"}},"created_at":"2026-07-05T04:25:42.822596+00:00","updated_at":"2026-07-05T04:25:42.822596+00:00"}