{"id":"W3204600540","doi":"10.6000/1929-6029.2021.10.11","title":"Fixed Effects High-Dimensional Profiling Models in Low Information Context","year":2021,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Healthcare Policy and Management","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Diabetes and Digestive and Kidney Diseases","keywords":"Profiling (computer programming); Medicaid; Inference; Payment; Medicine; Health care; Actuarial science; Computer science; Business; Artificial intelligence","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.004105421,0.00006438391,0.0002461491,0.0008343072,0.0000271833,0.00006740463,0.0003516678,0.00009486179,0.0003868284],"category_scores_gemma":[0.009741225,0.00006862721,0.0000322414,0.0002691296,0.00007189669,0.0003620879,0.0001549579,0.0008088199,0.00007725709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004367865,"about_ca_system_score_gemma":0.0005069614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001264405,"about_ca_topic_score_gemma":0.0004813119,"domain_scores_codex":[0.9976057,0.0001403987,0.001098701,0.0001170006,0.000771764,0.000266481],"domain_scores_gemma":[0.9976819,0.001121257,0.0002221592,0.00008474042,0.0007257875,0.0001641357],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00007621253,0.000161168,0.001606136,0.0001047263,0.00003589332,0.0009586499,0.0003556302,0.001371896,0.000002962659,0.9430612,0.001726154,0.05053943],"study_design_scores_gemma":[0.003681356,0.0001624585,0.01173399,0.0009780715,0.000001501746,0.00006282947,0.0002884149,0.1702453,0.0002866867,0.8030506,0.009347996,0.0001608069],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3950587,0.00213051,0.5259306,0.05927162,0.005728294,0.000697215,0.0007187276,0.00001291622,0.01045138],"genre_scores_gemma":[0.9908066,0.0006062848,0.007070256,0.001231872,0.0001629111,0.00001009103,0.00003996699,0.000006053119,0.00006589852],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5957479,"threshold_uncertainty_score":0.9986001,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08479732764426232,"score_gpt":0.400350894261346,"score_spread":0.3155535666170837,"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."}}