{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:5IX2JS52TBFDLK3NZLNUSGPRX6","short_pith_number":"pith:5IX2JS52","schema_version":"1.0","canonical_sha256":"ea2fa4cbba984a35ab6dcadb4919f1bfb38feca9cbcf06001030281d58b35279","source":{"kind":"arxiv","id":"2307.03083","version":1},"attestation_state":"computed","paper":{"title":"Predicting Opioid Use Outcomes in Minoritized Communities","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CE","authors_text":"Abhay Goyal, Christian Poellabauer, Frederick L Altice, Honoria Guarino, Koustuv Saha, Lam Yin Cheung, Navin Kumar, Nimay Parekh, Pedro Mateu Gelabert, Robin O'hanlon, Roger Ho Chun Man","submitted_at":"2023-07-06T15:50:58Z","abstract_excerpt":"Machine learning algorithms can sometimes exacerbate health disparities based on ethnicity, gender, and other factors. There has been limited work at exploring potential biases within algorithms deployed on a small scale, and/or within minoritized communities. Understanding the nature of potential biases may improve the prediction of various health outcomes. As a case study, we used data from a sample of 539 young adults from minoritized communities who engaged in nonmedical use of prescription opioids and/or heroin. We addressed the indicated issues through the following contributions: 1) Usi"},"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":"2307.03083","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CE","submitted_at":"2023-07-06T15:50:58Z","cross_cats_sorted":[],"title_canon_sha256":"f90f775e8aeb05597934b3d95b1ab52ba3247014288cecd26a03373cb2b3e004","abstract_canon_sha256":"5bd013a3b694388d6828801a07588e53bf3f81919ec27aa1e1e946f23be6d0ad"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:28:27.946908Z","signature_b64":"PkMyJnyoT8uwWUwSQivC6+n0mqkgK9eRTlwekvHmzQEYbBTYatZda5qXXzG8df2aesAG0NUrU9kDPmMMO/UXAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ea2fa4cbba984a35ab6dcadb4919f1bfb38feca9cbcf06001030281d58b35279","last_reissued_at":"2026-07-05T06:28:27.946439Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:28:27.946439Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Predicting Opioid Use Outcomes in Minoritized Communities","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CE","authors_text":"Abhay Goyal, Christian Poellabauer, Frederick L Altice, Honoria Guarino, Koustuv Saha, Lam Yin Cheung, Navin Kumar, Nimay Parekh, Pedro Mateu Gelabert, Robin O'hanlon, Roger Ho Chun Man","submitted_at":"2023-07-06T15:50:58Z","abstract_excerpt":"Machine learning algorithms can sometimes exacerbate health disparities based on ethnicity, gender, and other factors. There has been limited work at exploring potential biases within algorithms deployed on a small scale, and/or within minoritized communities. Understanding the nature of potential biases may improve the prediction of various health outcomes. As a case study, we used data from a sample of 539 young adults from minoritized communities who engaged in nonmedical use of prescription opioids and/or heroin. We addressed the indicated issues through the following contributions: 1) Usi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.03083","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/2307.03083/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":"2307.03083","created_at":"2026-07-05T06:28:27.946497+00:00"},{"alias_kind":"arxiv_version","alias_value":"2307.03083v1","created_at":"2026-07-05T06:28:27.946497+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.03083","created_at":"2026-07-05T06:28:27.946497+00:00"},{"alias_kind":"pith_short_12","alias_value":"5IX2JS52TBFD","created_at":"2026-07-05T06:28:27.946497+00:00"},{"alias_kind":"pith_short_16","alias_value":"5IX2JS52TBFDLK3N","created_at":"2026-07-05T06:28:27.946497+00:00"},{"alias_kind":"pith_short_8","alias_value":"5IX2JS52","created_at":"2026-07-05T06:28:27.946497+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/5IX2JS52TBFDLK3NZLNUSGPRX6","json":"https://pith.science/pith/5IX2JS52TBFDLK3NZLNUSGPRX6.json","graph_json":"https://pith.science/api/pith-number/5IX2JS52TBFDLK3NZLNUSGPRX6/graph.json","events_json":"https://pith.science/api/pith-number/5IX2JS52TBFDLK3NZLNUSGPRX6/events.json","paper":"https://pith.science/paper/5IX2JS52"},"agent_actions":{"view_html":"https://pith.science/pith/5IX2JS52TBFDLK3NZLNUSGPRX6","download_json":"https://pith.science/pith/5IX2JS52TBFDLK3NZLNUSGPRX6.json","view_paper":"https://pith.science/paper/5IX2JS52","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2307.03083&json=true","fetch_graph":"https://pith.science/api/pith-number/5IX2JS52TBFDLK3NZLNUSGPRX6/graph.json","fetch_events":"https://pith.science/api/pith-number/5IX2JS52TBFDLK3NZLNUSGPRX6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5IX2JS52TBFDLK3NZLNUSGPRX6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5IX2JS52TBFDLK3NZLNUSGPRX6/action/storage_attestation","attest_author":"https://pith.science/pith/5IX2JS52TBFDLK3NZLNUSGPRX6/action/author_attestation","sign_citation":"https://pith.science/pith/5IX2JS52TBFDLK3NZLNUSGPRX6/action/citation_signature","submit_replication":"https://pith.science/pith/5IX2JS52TBFDLK3NZLNUSGPRX6/action/replication_record"}},"created_at":"2026-07-05T06:28:27.946497+00:00","updated_at":"2026-07-05T06:28:27.946497+00:00"}