{"id":"W4416619207","doi":"10.5256/f1000research.10354.r22599","title":"Referee report. For: Revisiting inconsistency in large pharmacogenomic studies [version 1; referees: 1 approved, 1 approved with reservations, 1 not approved]","year":2017,"lang":"en","type":"article","venue":"Faculty of 1000 Research Ltd","topic":"Pharmacogenetics and Drug Metabolism","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Heart, Lung, and Blood Institute; Higher Education Discipline Innovation Project; National Natural Science Foundation of China; Government of Ontario; Canadian Cancer Society Research Institute; Canadian Institutes of Health Research; Cancer Research Society; National Supercomputer Centre in Guangzhou; National Cancer Institute; Yale University","keywords":"MEDLINE; Precision medicine; Matching (statistics); Pharmacogenomics; Set (abstract data type)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.05051766,0.002171642,0.003162996,0.009000115,0.006123161,0.006955241,0.007391719,0.01520209,0.2934857],"category_scores_gemma":[0.5554377,0.001598056,0.004230629,0.007488682,0.002447187,0.004807813,0.006608155,0.01118471,0.1396866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007574137,"about_ca_system_score_gemma":0.01310061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02544272,"about_ca_topic_score_gemma":0.02984915,"domain_scores_codex":[0.9528597,0.01196678,0.01204172,0.004099847,0.0171772,0.001854741],"domain_scores_gemma":[0.36964,0.1678293,0.01986023,0.03029903,0.4050948,0.007276645],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001442997,0.000005164915,0.00004251282,0.0001971247,0.00001086938,0.00003333282,0.00003083813,0.000008171621,0.00004220427,0.0001449363,0.9969952,0.002475235],"study_design_scores_gemma":[0.0002915822,0.0000357016,0.002107685,0.002338523,0.0001221568,0.0002598312,0.0002956158,0.0001909003,0.0004168163,0.003109418,0.9906839,0.0001478472],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"commentary","genre_scores_codex":[0.0003962371,0.003795172,0.007713696,0.3021674,0.616751,0.004134705,0.03607959,0.004657962,0.02430431],"genre_scores_gemma":[0.009808712,0.005640324,0.02513361,0.5188871,0.2049722,0.01398338,0.02764489,0.006981256,0.1869485],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.9494823,"threshold_uncertainty_score":0.9818074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3215265235399312,"score_gpt":0.5278834465567952,"score_spread":0.206356923016864,"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."}}