{"id":"W4311917936","doi":"10.1016/j.annonc.2022.12.004","title":"Methodological and reporting standards for quality-of-life data eligible for European Society for Medical Oncology-Magnitude of Clinical Benefit Scale (ESMO-MCBS) credit","year":2022,"lang":"en","type":"article","venue":"Annals of Oncology","topic":"Economic and Financial Impacts of Cancer","field":"Economics, Econometrics and Finance","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Genentech; Ipsen; European Society for Medical Oncology; Chugai Pharmaceutical; Seagen; Regeneron Pharmaceuticals; Servier; G1 Therapeutics; Amgen; Pfizer; World Health Organization; Radius Health; AstraZeneca; Eli Lilly and Company","keywords":"Medicine; Checklist; Quality of life (healthcare); Clinical trial; Clinical endpoint; Scale (ratio); Test (biology); Medical physics; Intensive care medicine; Internal medicine; Nursing","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":["metaresearch"],"category_scores_codex":[0.05691615,0.000169212,0.002451195,0.00009782738,0.0001786909,0.000009197715,0.00071649,0.0003286055,0.0001825244],"category_scores_gemma":[0.03555665,0.0001831315,0.0007051299,0.0001027323,0.0004916146,0.000161091,0.0006431837,0.0002899782,6.336703e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007357797,"about_ca_system_score_gemma":0.001020023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001723712,"about_ca_topic_score_gemma":0.0001018119,"domain_scores_codex":[0.993164,0.0002038462,0.005372642,0.000709158,0.0001135073,0.000436836],"domain_scores_gemma":[0.9859892,0.005697942,0.007044166,0.0005483211,0.0005029658,0.0002173743],"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":[0.008484947,0.00259129,0.1095781,0.001616774,0.00168755,0.000005144176,0.00156875,0.0002719102,0.0001170372,0.2120464,0.2510306,0.4110015],"study_design_scores_gemma":[0.004899588,0.007109034,0.0257861,0.00002150388,0.000058051,0.000006674636,0.0005140417,0.001824505,0.0001068409,0.04557785,0.9138249,0.0002709005],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8722883,0.005554339,0.07351094,0.01353,0.001881713,0.001729592,0.02030165,0.0000305962,0.01117285],"genre_scores_gemma":[0.8557453,0.00431486,0.1310652,0.006251064,0.001363882,0.0003195233,0.0005252595,0.0000810468,0.0003338644],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6627944,"threshold_uncertainty_score":0.9725673,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7311834190191645,"score_gpt":0.5746809436174326,"score_spread":0.1565024754017319,"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."}}