{"id":"W4410010091","doi":"10.1097/mlr.0000000000002161","title":"Approaches to Identify Nursing Home Specialists Using Medicare Claims Data","year":2025,"lang":"en","type":"article","venue":"Medical Care","topic":"Geriatric Care and Nursing Homes","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Institute on Aging","keywords":"Nursing; Nursing homes; MEDLINE; Medicine; Family medicine; Data science; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06122038,0.001397838,0.001373011,0.02691657,0.001097719,0.004809794,0.002853838,0.00161422,0.002080457],"category_scores_gemma":[0.1774545,0.001027997,0.002909535,0.0163643,0.0007293521,0.002525029,0.005048374,0.00178808,0.0008227461],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002693562,"about_ca_system_score_gemma":0.005189777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03794274,"about_ca_topic_score_gemma":0.04171582,"domain_scores_codex":[0.9343809,0.03473406,0.009472883,0.009643078,0.01018392,0.001585181],"domain_scores_gemma":[0.8449392,0.07363611,0.04677711,0.01249567,0.02028587,0.001866099],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002135461,0.0001892034,0.9392648,0.0006216303,0.001519838,0.0001339085,0.001620243,0.002184617,0.0002397389,0.00201835,0.004259756,0.04773444],"study_design_scores_gemma":[0.0001532614,0.0003715434,0.9137449,0.001863974,0.0007447359,0.0005485303,0.00493075,0.04541835,0.001432987,0.01118468,0.01941002,0.0001961872],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.732735,0.00861108,0.1520895,0.008253249,0.0006729867,0.008350652,0.0558967,0.001052405,0.03233846],"genre_scores_gemma":[0.8702273,0.001185253,0.1059392,0.001162555,0.0002530377,0.003384032,0.01658465,0.00007766245,0.001186268],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06122038,"threshold_uncertainty_score":0.3237682,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3260713200265821,"score_gpt":0.5136897447749019,"score_spread":0.1876184247483198,"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."}}