{"id":"W4413295453","doi":"10.2196/68345","title":"Automating Individualized Notification of Drug Recalls to Patients: Complex Challenges and Qualitative Evaluation","year":2025,"lang":"en","type":"article","venue":"JMIRx Med","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of California, San Francisco; U.S. Department of Health and Human Services","keywords":"Preprint; Qualitative research; Drug; Computer science; Medicine; World Wide Web; Pharmacology; Sociology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007167283,0.000131973,0.0004121639,0.0002593011,0.0002802056,0.000004621038,0.0001330972,0.0001399482,0.00008238829],"category_scores_gemma":[0.002961337,0.0001225229,0.00002683643,0.0003148234,0.00003277369,0.00008886273,0.00007488372,0.0002724653,0.00004320782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004525445,"about_ca_system_score_gemma":0.0005847609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003987677,"about_ca_topic_score_gemma":0.000926378,"domain_scores_codex":[0.9929811,0.004753225,0.00108057,0.0003206165,0.0004841966,0.0003802794],"domain_scores_gemma":[0.9956292,0.002455521,0.0005787152,0.0003037241,0.0009240831,0.0001087626],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0001778131,0.0002632871,0.01179294,0.004704332,0.0001186345,8.75248e-8,0.582383,0.000003747876,0.001751811,0.02491843,0.02052193,0.353364],"study_design_scores_gemma":[0.006103759,0.0002670755,0.8813272,0.003180555,0.00009306526,1.146855e-7,0.06987704,0.002625667,0.0002133211,0.006160651,0.02980269,0.0003488995],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9645531,0.001068006,0.0002453773,0.01752911,0.0005510794,0.005480726,0.00003040482,0.0001040802,0.01043819],"genre_scores_gemma":[0.993273,0.0001162005,0.002317596,0.0005866455,0.00006131716,0.001709322,0.0001091328,0.00001919895,0.00180763],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8695342,"threshold_uncertainty_score":0.4996338,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2249843076344837,"score_gpt":0.5623899179541543,"score_spread":0.3374056103196705,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}