{"id":"W2139672960","doi":"10.1186/1477-7525-12-35","title":"Mapping EORTC QLQ-C30 and QLQ-MY20 to EQ-5D in patients with multiple myeloma","year":2014,"lang":"en","type":"article","venue":"Health and Quality of Life Outcomes","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":78,"is_retracted":false,"has_abstract":true,"ca_institutions":"TransCanada (Canada); EVRAZ (Canada)","funders":"","keywords":"Medicine; Quality of life (healthcare); EQ-5D; Multiple myeloma; Logistic regression; Multivariate statistics; Physical therapy; Multivariate analysis; Cohort; Health related quality of life; Internal medicine; Disease; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004764219,0.0004461992,0.0005376803,0.0009883812,0.0002429262,0.0006475498,0.0006490345,0.0005080487,0.0008065869],"category_scores_gemma":[0.01444772,0.0002415909,0.0007297276,0.001290388,0.0002666291,0.0005282812,0.001122102,0.0007463366,0.0002329679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007663746,"about_ca_system_score_gemma":0.0006485243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005294183,"about_ca_topic_score_gemma":0.003296874,"domain_scores_codex":[0.9986516,0.0008531509,0.00007084538,0.0001982899,0.0001386173,0.00008761262],"domain_scores_gemma":[0.9970893,0.001965802,0.0004687969,0.0002154606,0.0001752235,0.00008541399],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005235888,0.00007599212,0.9457121,0.00005949798,0.0001178144,0.0001509876,0.0003044416,0.0142808,0.0003746928,0.00009346432,0.0003909652,0.0379157],"study_design_scores_gemma":[0.000120484,0.0006227492,0.8744035,0.00005448994,0.0001313084,0.0006028804,0.0006708843,0.1204289,0.0008597631,0.001283526,0.0007973724,0.00002420136],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.992498,0.0004816593,0.006147732,0.000202882,0.000007246018,0.00005264309,0.0002744768,0.00003259,0.0003027861],"genre_scores_gemma":[0.9963753,0.0001100133,0.003112085,0.00002548837,0.00000371398,0.00003565052,0.00026191,0.000005012753,0.00007089724],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005294183,"threshold_uncertainty_score":0.0251959,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3849404613044445,"score_gpt":0.4064926507346145,"score_spread":0.02155218943017007,"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."}}