{"id":"W2064530264","doi":"10.1002/sim.1060","title":"Longitudinal profiles of health care providers","year":2002,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Healthcare Policy and Management","field":"Economics, Econometrics and Finance","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; University of Toronto","funders":"Harvard University","keywords":"Mahalanobis distance; Univariate; Health care; Computer science; Profiling (computer programming); Baseline (sea); Longitudinal data; Statistics; Data mining; Multivariate statistics; Econometrics; Actuarial science; Machine learning; Artificial intelligence; Business; Mathematics","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":[0.0005160736,0.00008367313,0.0003974992,0.0002410657,0.000037499,0.000003585458,0.0001016063,0.0000305049,0.000541812],"category_scores_gemma":[0.0003575499,0.00008646825,0.00001375512,0.0001891287,0.0001089824,0.00003432404,0.00002334274,0.0001079166,0.00005030162],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001246707,"about_ca_system_score_gemma":0.00001767634,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007656734,"about_ca_topic_score_gemma":0.0006996508,"domain_scores_codex":[0.9987248,0.00002469836,0.0007550836,0.0002021048,0.00005216142,0.0002411255],"domain_scores_gemma":[0.999366,0.00006644782,0.0002893531,0.0001879622,0.00002935209,0.00006084274],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000003855819,0.00004339041,0.02735319,0.0008242987,0.00001372379,0.000008116267,0.007043887,0.00001286151,2.152869e-7,0.9033857,0.03529081,0.02601997],"study_design_scores_gemma":[0.004882968,0.002542819,0.2520888,0.0008423472,0.00001826136,0.000006895807,0.009393469,0.00585514,0.00001484705,0.215695,0.5079113,0.0007481587],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02058241,0.1273693,0.3772147,0.204748,0.004055392,0.004890196,0.004754199,0.0001698963,0.2562159],"genre_scores_gemma":[0.9894667,0.001078259,0.007958016,0.0009115657,0.0000829111,0.00002266338,0.00003733025,0.00001193944,0.000430668],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9688842,"threshold_uncertainty_score":0.9989514,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09542272310886057,"score_gpt":0.3368681650707985,"score_spread":0.2414454419619379,"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."}}