{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:NQPNE63MNATITACTI5NFOO5BAM","short_pith_number":"pith:NQPNE63M","schema_version":"1.0","canonical_sha256":"6c1ed27b6c6826898053475a573ba1030c9410e7fa7d3bc42425f77a3b00f418","source":{"kind":"arxiv","id":"2412.11348","version":4},"attestation_state":"computed","paper":{"title":"Analyzing zero-inflated clustered longitudinal ordinal outcomes using GEE-type models with an application to dental fluorosis studies","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Anish Mukherjee, Jeremy T. Gaskins, Peihua Qiu, Shoumi Sarkar, Somnath Datta, Steven Levy","submitted_at":"2024-12-16T00:12:37Z","abstract_excerpt":"Motivated by the Iowa Fluoride Study (IFS), which tracked fluoride intake and dental outcomes from childhood to young adulthood (ages 9, 13, 17, and 23), we analyze dental fluorosis - a condition caused by excessive fluoride exposure during enamel formation. In this context, fluorosis scores across tooth surfaces present as zero-inflated, clustered, and longitudinal ordinal outcomes, prompting the development of a unified modeling framework. Leveraging generalized estimating equations (GEEs), we construct separate models for the presence and severity of fluorosis and propose a combined model t"},"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":"2412.11348","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2024-12-16T00:12:37Z","cross_cats_sorted":[],"title_canon_sha256":"d64fa14a50971fe40524042e9e5bc1d98870ce449227e17b1f615a419f6f5dab","abstract_canon_sha256":"d7b4d48aefd252f81e99efedc000a5db82f5d6be87cdb1993cbc8a9359dc0672"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:50:20.530336Z","signature_b64":"r9qtRqDcyaMN0MmwrnxKXG2GnmpOxOKyvJgVPw58w9/v+tQmAt3tZwGBrtqmduSSGYrkMqAI/frJGhslihA6DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6c1ed27b6c6826898053475a573ba1030c9410e7fa7d3bc42425f77a3b00f418","last_reissued_at":"2026-07-05T11:50:20.529821Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:50:20.529821Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Analyzing zero-inflated clustered longitudinal ordinal outcomes using GEE-type models with an application to dental fluorosis studies","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Anish Mukherjee, Jeremy T. Gaskins, Peihua Qiu, Shoumi Sarkar, Somnath Datta, Steven Levy","submitted_at":"2024-12-16T00:12:37Z","abstract_excerpt":"Motivated by the Iowa Fluoride Study (IFS), which tracked fluoride intake and dental outcomes from childhood to young adulthood (ages 9, 13, 17, and 23), we analyze dental fluorosis - a condition caused by excessive fluoride exposure during enamel formation. In this context, fluorosis scores across tooth surfaces present as zero-inflated, clustered, and longitudinal ordinal outcomes, prompting the development of a unified modeling framework. Leveraging generalized estimating equations (GEEs), we construct separate models for the presence and severity of fluorosis and propose a combined model t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.11348","kind":"arxiv","version":4},"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/2412.11348/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":"2412.11348","created_at":"2026-07-05T11:50:20.529885+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.11348v4","created_at":"2026-07-05T11:50:20.529885+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.11348","created_at":"2026-07-05T11:50:20.529885+00:00"},{"alias_kind":"pith_short_12","alias_value":"NQPNE63MNATI","created_at":"2026-07-05T11:50:20.529885+00:00"},{"alias_kind":"pith_short_16","alias_value":"NQPNE63MNATITACT","created_at":"2026-07-05T11:50:20.529885+00:00"},{"alias_kind":"pith_short_8","alias_value":"NQPNE63M","created_at":"2026-07-05T11:50:20.529885+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.06135","citing_title":"Linked-Tucker Factorized Individualized Regression for Paired Multivariate Categorical Outcomes","ref_index":6,"is_internal_anchor":false},{"citing_arxiv_id":"2605.06135","citing_title":"Linked-Tucker Factorized Individualized Regression for Paired Multivariate Categorical Outcomes","ref_index":6,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NQPNE63MNATITACTI5NFOO5BAM","json":"https://pith.science/pith/NQPNE63MNATITACTI5NFOO5BAM.json","graph_json":"https://pith.science/api/pith-number/NQPNE63MNATITACTI5NFOO5BAM/graph.json","events_json":"https://pith.science/api/pith-number/NQPNE63MNATITACTI5NFOO5BAM/events.json","paper":"https://pith.science/paper/NQPNE63M"},"agent_actions":{"view_html":"https://pith.science/pith/NQPNE63MNATITACTI5NFOO5BAM","download_json":"https://pith.science/pith/NQPNE63MNATITACTI5NFOO5BAM.json","view_paper":"https://pith.science/paper/NQPNE63M","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.11348&json=true","fetch_graph":"https://pith.science/api/pith-number/NQPNE63MNATITACTI5NFOO5BAM/graph.json","fetch_events":"https://pith.science/api/pith-number/NQPNE63MNATITACTI5NFOO5BAM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NQPNE63MNATITACTI5NFOO5BAM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NQPNE63MNATITACTI5NFOO5BAM/action/storage_attestation","attest_author":"https://pith.science/pith/NQPNE63MNATITACTI5NFOO5BAM/action/author_attestation","sign_citation":"https://pith.science/pith/NQPNE63MNATITACTI5NFOO5BAM/action/citation_signature","submit_replication":"https://pith.science/pith/NQPNE63MNATITACTI5NFOO5BAM/action/replication_record"}},"created_at":"2026-07-05T11:50:20.529885+00:00","updated_at":"2026-07-05T11:50:20.529885+00:00"}