{"id":"W7098616871","doi":"","title":"STRENGTHENING NEW YORK’S EPIC PROGRAM: OPTIONS FOR IMPROVING DRUG COVERAGE FOR MEDICARE BENEFICIARIES","year":2003,"lang":"en","type":"article","venue":"","topic":"Diverse Scientific and Economic Studies","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Commonwealth; EPIC; Quarter (Canadian coin); Commission","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001977481,0.0002202318,0.0001972029,0.0008076899,0.0005090805,0.0008037298,0.0007199239,0.0009860783,0.04316483],"category_scores_gemma":[0.006752239,0.00008233526,0.0003025768,0.0006292231,0.0002470816,0.001206919,0.001659793,0.0007014725,0.002610236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001105721,"about_ca_system_score_gemma":0.006587937,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02562558,"about_ca_topic_score_gemma":0.07250362,"domain_scores_codex":[0.9991705,0.0004276934,0.00002518855,0.00004023983,0.0001719741,0.0001643945],"domain_scores_gemma":[0.9973852,0.0005590436,0.0002764219,0.0001250706,0.0003215125,0.001332791],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0014179,0.002559813,0.03345391,0.000359691,0.000102668,0.0001666272,0.0003136984,0.001171187,0.001092844,0.006791318,0.2498535,0.7027169],"study_design_scores_gemma":[0.003608857,0.004517921,0.4496653,0.0011834,0.0006394223,0.0005850314,0.001263655,0.007239302,0.002520573,0.00662485,0.5220658,0.00008578865],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"other","genre_scores_codex":[0.3905665,0.01046029,0.009255978,0.2697114,0.00156982,0.002124005,0.01081455,0.001184997,0.3043124],"genre_scores_gemma":[0.82285,0.007696498,0.01225835,0.02636879,0.001877558,0.001372757,0.007185448,0.0001229593,0.1202677],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.04316483,"threshold_uncertainty_score":0.1444007,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05223021151692822,"score_gpt":0.2351662479291869,"score_spread":0.1829360364122586,"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."}}