{"id":"W4225476177","doi":"10.2196/35623","title":"Identifying Family and Unpaid Caregivers in Electronic Health Records: Descriptive Analysis","year":2022,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. Department of Veterans Affairs","keywords":"Veterans Affairs; Referral; Descriptive statistics; Phone; Medicine; Family caregivers; Logistic regression; Family medicine; Medical record; Gerontology; Nursing","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":["sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.01650053,0.0002349154,0.0007798304,0.002810638,0.00385463,0.00003425051,0.0005029915,0.0001333333,0.0005153813],"category_scores_gemma":[0.0001594431,0.0002384256,0.0001197185,0.00616069,0.0001490674,0.0004881986,0.0007658321,0.005664359,0.0001146897],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01132756,"about_ca_system_score_gemma":0.005286326,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02501875,"about_ca_topic_score_gemma":0.01891869,"domain_scores_codex":[0.9811083,0.01245998,0.001182101,0.0006306091,0.001421881,0.003197134],"domain_scores_gemma":[0.9968787,0.001439866,0.0004356081,0.0004892601,0.0003575782,0.0003990147],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.000779523,0.0004106869,0.1172767,0.002433101,0.0007579141,0.00003825112,0.769372,0.0001132305,0.0001616905,0.01418704,0.07203729,0.02243258],"study_design_scores_gemma":[0.002583857,0.002260165,0.08916052,0.0002968664,0.00002478418,0.000007675896,0.8195469,0.004028476,0.000005497451,0.002826166,0.0788417,0.0004173857],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9779046,0.005226084,0.0005479953,0.00266517,0.0004445259,0.004287454,0.0001034654,0.0001116608,0.008709044],"genre_scores_gemma":[0.9926447,0.001237741,0.00004376842,0.0003426621,0.00007730035,0.003595558,0.00007044105,0.00004110314,0.001946718],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05017492,"threshold_uncertainty_score":0.9989835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1637989893970321,"score_gpt":0.5205409461514022,"score_spread":0.3567419567543701,"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."}}