{"id":"W2607065805","doi":"10.1017/s0266462317000149","title":"DRUG EVALUATION AND DECISION MAKING IN CATALONIA: DEVELOPMENT AND VALIDATION OF A METHODOLOGICAL FRAMEWORK BASED ON MULTI-CRITERIA DECISION ANALYSIS (MCDA) FOR ORPHAN DRUGS","year":2017,"lang":"en","type":"article","venue":"International Journal of Technology Assessment in Health Care","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":89,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Multiple-criteria decision analysis; Orphan drug; Context (archaeology); Population; Medicine; Management science; Decision analysis; Reliability (semiconductor); Computer science; Risk analysis (engineering); Operations research; Engineering; Environmental health; Statistics; 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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02445108,0.000135496,0.0008746604,0.002471063,0.0001630424,0.00008000774,0.0004313138,0.0002189734,0.00002565794],"category_scores_gemma":[0.009274278,0.0001476999,0.00008185373,0.0001920322,0.00007164745,0.0002975522,0.000100719,0.0003157264,0.000001052262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00145035,"about_ca_system_score_gemma":0.0003996989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001183611,"about_ca_topic_score_gemma":0.000408541,"domain_scores_codex":[0.9954622,0.0002907489,0.003411884,0.000364337,0.0002848203,0.0001860303],"domain_scores_gemma":[0.9922071,0.002587138,0.004417752,0.0002847773,0.0004449791,0.00005823346],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003357566,0.0002403554,0.8702581,0.0002432454,0.0001787622,0.000004371741,0.002513597,0.005756374,0.000003755901,0.01011747,0.00005632489,0.1102919],"study_design_scores_gemma":[0.003829261,0.0002646848,0.8468021,0.00196446,0.00002980236,0.000007587229,0.00310727,0.1006766,0.00007131987,0.04263008,0.0004043264,0.000212481],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7575305,0.0009243108,0.2273651,0.0131815,0.0004188606,0.000511173,0.00005421967,0.000005293935,0.000009075916],"genre_scores_gemma":[0.6356578,0.00005574803,0.3638006,0.0003952875,0.00002570984,0.00004110833,0.00001670695,0.000006470347,4.888541e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1364355,"threshold_uncertainty_score":0.999071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3411429897501823,"score_gpt":0.5784186831033785,"score_spread":0.2372756933531963,"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."}}