{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006082628,0.000190614,0.0003562117,0.004409778,0.000535669,0.0009496422,0.000419277,0.0002559496,0.001171489],"category_scores_gemma":[0.02167917,0.0002740131,0.0006112482,0.003965706,0.0004219792,0.001174401,0.0008726402,0.00036491,0.0002213229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009729527,"about_ca_system_score_gemma":0.001753625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008089658,"about_ca_topic_score_gemma":0.007883831,"domain_scores_codex":[0.9956937,0.001195604,0.001162675,0.0003971503,0.001190121,0.0003606897],"domain_scores_gemma":[0.9839204,0.008132533,0.004713411,0.0008392996,0.002062871,0.0003315248],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006444715,0.00004886757,0.9954584,0.00003392329,0.00002337398,0.00004834936,0.0008913708,0.00004379671,0.00006169244,0.00005416209,0.0002130393,0.003058752],"study_design_scores_gemma":[0.00001234136,0.000154588,0.9863806,0.000101184,0.00004499153,0.0003441162,0.01021255,0.001018308,0.000347338,0.0001188472,0.001250709,0.00001436313],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9952437,0.0001741687,0.0008796997,0.00008337909,0.000003693848,0.0002549911,0.00263236,0.00001085563,0.000717074],"genre_scores_gemma":[0.9966339,0.0001735606,0.001044071,0.00005211348,0.000005358022,0.0003432194,0.001563785,0.000005319038,0.0001787286],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008089658,"threshold_uncertainty_score":0.03216839,"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."}}