{"id":"W803060718","doi":"10.1212/wnl.84.14_supplement.p7.336","title":"Predicting Patient Attendance in the Neuromuscular Clinic: A Logistic Regression Analysis (P7.336)","year":2015,"lang":"en","type":"article","venue":"Neurology","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Logistic regression; Attendance; Medicine; Emergency medicine; Physical therapy; Internal medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001567982,0.0001246642,0.000285798,0.0001993335,0.0004247736,0.00001097063,0.0002008003,0.0002384215,0.000073471],"category_scores_gemma":[0.001896262,0.00008142123,0.00006767946,0.0008929635,0.00005302658,0.00007006929,0.0000759275,0.001011845,0.00009691263],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003221884,"about_ca_system_score_gemma":0.0002400125,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006881756,"about_ca_topic_score_gemma":0.002821761,"domain_scores_codex":[0.9947842,0.003501846,0.0006935748,0.0003679002,0.0002498585,0.0004026604],"domain_scores_gemma":[0.9980952,0.0008276601,0.0002415732,0.0004869729,0.0002367364,0.0001118147],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001615814,0.00008595254,0.9687194,0.00001874422,0.00001988289,0.00006653266,0.005041135,0.02406548,0.000008574119,0.0002683433,0.0007460733,0.000798245],"study_design_scores_gemma":[0.001698497,0.002134133,0.6758083,0.0000605012,0.0002226545,0.00001887779,0.001676218,0.2985625,0.000001561812,0.0004071779,0.01913445,0.0002751257],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9811564,0.0001018631,0.001990429,0.01420771,0.0007295138,0.000599461,0.000005979677,0.00005425845,0.001154384],"genre_scores_gemma":[0.986573,0.00004055716,0.0005358541,0.01244307,0.0001512032,0.0001434445,0.00004733905,0.00001431607,0.00005122712],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2929112,"threshold_uncertainty_score":0.439602,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1837880263993039,"score_gpt":0.4587820909482748,"score_spread":0.274994064548971,"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."}}