{"id":"W2107036614","doi":"10.1111/j.1475-6773.2010.01111.x","title":"The Relationship between Health Plan Performance Measures and Physician Network Overlap: Implications for Measuring Plan Quality","year":2010,"lang":"en","type":"article","venue":"Health Services Research","topic":"Healthcare Policy and Management","field":"Economics, Econometrics and Finance","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Canadian Institutes of Health Research; Agency for Healthcare Research and Quality","keywords":"Plan (archaeology); Quality (philosophy); Incentive; Quality assurance; Incentive program; Health care; Health plan; Pay for performance; Sample (material); Quality management; Data collection; Medicine; Business; Marketing; Statistics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.03335699,0.0003794204,0.0005480468,0.003852923,0.0006541415,0.001895682,0.0009053901,0.0006128775,0.001750588],"category_scores_gemma":[0.2392768,0.0003124692,0.0003990945,0.005920286,0.001857244,0.003478727,0.003434771,0.0009159214,0.0001329406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002027181,"about_ca_system_score_gemma":0.001075401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008292234,"about_ca_topic_score_gemma":0.005441592,"domain_scores_codex":[0.9690049,0.01833354,0.002556017,0.002694284,0.006212454,0.001198772],"domain_scores_gemma":[0.5357049,0.3598157,0.08298264,0.008873928,0.009723515,0.002899392],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004344786,0.00002677095,0.992843,0.00001876552,0.0001175648,0.00001115221,0.0002275646,0.002089572,0.00004771574,0.0007571826,0.00009912458,0.003717982],"study_design_scores_gemma":[0.00001418486,0.00008511186,0.978865,0.00003078402,0.00003763452,0.00005271613,0.0004574103,0.01704033,0.0002497486,0.002779937,0.0003758859,0.00001132353],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9728667,0.0005409922,0.02102729,0.001282522,0.0000108115,0.00007556422,0.000514043,0.00003878766,0.003643392],"genre_scores_gemma":[0.9976413,0.00004018073,0.001944034,0.00004070864,0.00002184609,0.00004125667,0.0001976353,0.000004287903,0.00006871219],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03335699,"threshold_uncertainty_score":0.1764107,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4471863039277982,"score_gpt":0.4550231672755022,"score_spread":0.007836863347704004,"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."}}