{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:3ECS3VRE4ZV3MBFZV36JQPILVI","short_pith_number":"pith:3ECS3VRE","schema_version":"1.0","canonical_sha256":"d9052dd624e66bb604b9aefc983d0baa3107e580a7a0f5ac37c37be2673b0391","source":{"kind":"arxiv","id":"2401.09637","version":2},"attestation_state":"computed","paper":{"title":"Impact of Large Language Model Assistance on Patients Reading Clinical Notes: A Mixed-Methods Study","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.HC","authors_text":"Alejandro Buendia, Barbara Lam, Catherine E. Ricciardi, Chloe O'Connell, David Sontag, Elizabeth Bondi-Kelly, Hussein Mozannar, Irbaz B. Riaz, Marzyeh Ghassemi, Mercy Asiedu, Monica Agrawal, Niklas Mannhardt, Tatiana Urman","submitted_at":"2024-01-17T23:14:52Z","abstract_excerpt":"Large language models (LLMs) have immense potential to make information more accessible, particularly in medicine, where complex medical jargon can hinder patient comprehension of clinical notes. We developed a patient-facing tool using LLMs to make clinical notes more readable by simplifying, extracting information from, and adding context to the notes. We piloted the tool with clinical notes donated by patients with a history of breast cancer and synthetic notes from a clinician. Participants (N=200, healthy, female-identifying patients) were randomly assigned three clinical notes in our too"},"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":"2401.09637","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.HC","submitted_at":"2024-01-17T23:14:52Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"a46b5f6f4ed0681a4b5e71824e7f4cf936093494e827fa542aa8f8b11097e37f","abstract_canon_sha256":"333a746635a914e82a1d295ff8c4b291cdd365550c9f0795c00d1eb978a78f18"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:20:22.418447Z","signature_b64":"m8uS9HLMuHlwtQzR+807GAnJJ4VUwQHQJjgoYbaIP6bX4mcxn9jyBW+LV9nlzK+yl6mKWkjBZd/c3WJjThFFDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d9052dd624e66bb604b9aefc983d0baa3107e580a7a0f5ac37c37be2673b0391","last_reissued_at":"2026-07-05T09:20:22.417857Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:20:22.417857Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Impact of Large Language Model Assistance on Patients Reading Clinical Notes: A Mixed-Methods Study","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.HC","authors_text":"Alejandro Buendia, Barbara Lam, Catherine E. Ricciardi, Chloe O'Connell, David Sontag, Elizabeth Bondi-Kelly, Hussein Mozannar, Irbaz B. Riaz, Marzyeh Ghassemi, Mercy Asiedu, Monica Agrawal, Niklas Mannhardt, Tatiana Urman","submitted_at":"2024-01-17T23:14:52Z","abstract_excerpt":"Large language models (LLMs) have immense potential to make information more accessible, particularly in medicine, where complex medical jargon can hinder patient comprehension of clinical notes. We developed a patient-facing tool using LLMs to make clinical notes more readable by simplifying, extracting information from, and adding context to the notes. We piloted the tool with clinical notes donated by patients with a history of breast cancer and synthetic notes from a clinician. Participants (N=200, healthy, female-identifying patients) were randomly assigned three clinical notes in our too"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.09637","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/2401.09637/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":"2401.09637","created_at":"2026-07-05T09:20:22.417923+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.09637v2","created_at":"2026-07-05T09:20:22.417923+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.09637","created_at":"2026-07-05T09:20:22.417923+00:00"},{"alias_kind":"pith_short_12","alias_value":"3ECS3VRE4ZV3","created_at":"2026-07-05T09:20:22.417923+00:00"},{"alias_kind":"pith_short_16","alias_value":"3ECS3VRE4ZV3MBFZ","created_at":"2026-07-05T09:20:22.417923+00:00"},{"alias_kind":"pith_short_8","alias_value":"3ECS3VRE","created_at":"2026-07-05T09:20:22.417923+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.18285","citing_title":"RELIANCE: Curating and Evaluating Reproductive Health Information on Social Media","ref_index":31,"is_internal_anchor":false},{"citing_arxiv_id":"2502.16022","citing_title":"Enhancing LLMs for Identifying and Prioritizing Important Medical Jargons from Electronic Health Record Notes Utilizing Data Augmentation: A Comparative Study","ref_index":71,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3ECS3VRE4ZV3MBFZV36JQPILVI","json":"https://pith.science/pith/3ECS3VRE4ZV3MBFZV36JQPILVI.json","graph_json":"https://pith.science/api/pith-number/3ECS3VRE4ZV3MBFZV36JQPILVI/graph.json","events_json":"https://pith.science/api/pith-number/3ECS3VRE4ZV3MBFZV36JQPILVI/events.json","paper":"https://pith.science/paper/3ECS3VRE"},"agent_actions":{"view_html":"https://pith.science/pith/3ECS3VRE4ZV3MBFZV36JQPILVI","download_json":"https://pith.science/pith/3ECS3VRE4ZV3MBFZV36JQPILVI.json","view_paper":"https://pith.science/paper/3ECS3VRE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.09637&json=true","fetch_graph":"https://pith.science/api/pith-number/3ECS3VRE4ZV3MBFZV36JQPILVI/graph.json","fetch_events":"https://pith.science/api/pith-number/3ECS3VRE4ZV3MBFZV36JQPILVI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3ECS3VRE4ZV3MBFZV36JQPILVI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3ECS3VRE4ZV3MBFZV36JQPILVI/action/storage_attestation","attest_author":"https://pith.science/pith/3ECS3VRE4ZV3MBFZV36JQPILVI/action/author_attestation","sign_citation":"https://pith.science/pith/3ECS3VRE4ZV3MBFZV36JQPILVI/action/citation_signature","submit_replication":"https://pith.science/pith/3ECS3VRE4ZV3MBFZV36JQPILVI/action/replication_record"}},"created_at":"2026-07-05T09:20:22.417923+00:00","updated_at":"2026-07-05T09:20:22.417923+00:00"}