{"id":"W2031877590","doi":"10.1007/s10198-014-0633-1","title":"Between-country heterogeneity in EQ-5D-3L scoring algorithms: how much is due to differences in health state selection?","year":2014,"lang":"en","type":"review","venue":"The European Journal of Health Economics","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University; SickKids Foundation; Hospital for Sick Children","funders":"Canadian Institutes of Health Research","keywords":"Selection (genetic algorithm); State (computer science); Computer science; Algorithm; Econometrics; Machine learning; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.1081208,0.0007608812,0.008136947,0.001888602,0.000397693,0.0003438773,0.001588216,0.0001660806,0.00003567514],"category_scores_gemma":[0.001275111,0.0007500569,0.0005364587,0.0006577199,0.00009691944,0.000524165,0.0002250893,0.001710627,0.0005649613],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004694903,"about_ca_system_score_gemma":0.002260073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001234083,"about_ca_topic_score_gemma":0.00127028,"domain_scores_codex":[0.9701751,0.009203427,0.01793291,0.001084289,0.0001686733,0.001435547],"domain_scores_gemma":[0.9772965,0.001931343,0.01912156,0.0008535502,0.00009068099,0.0007063495],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004204875,0.0003376577,0.03519939,0.0418096,0.0007731874,0.00002652386,0.007282152,0.0008662769,8.873946e-9,0.002581599,0.01698264,0.8940989],"study_design_scores_gemma":[0.0009916238,0.0005807871,0.01296634,0.009749442,0.00002273895,0.000148753,0.0004739321,0.0003346633,8.143184e-8,0.001028301,0.9728932,0.0008101523],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.01315715,0.9404799,0.004579233,0.03678063,0.001764224,0.002289688,0.0006820163,0.00002986004,0.0002373158],"genre_scores_gemma":[0.00501579,0.9813316,0.001959547,0.009348099,0.001940405,0.00003768207,0.00003005294,0.0002086054,0.0001281649],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9559106,"threshold_uncertainty_score":0.999495,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5538628616408081,"score_gpt":0.4444111985412612,"score_spread":0.109451663099547,"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."}}