{"id":"W2011474181","doi":"10.1038/nature12831","title":"Inconsistency in large pharmacogenomic studies","year":2013,"lang":"en","type":"article","venue":"Nature","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":534,"is_retracted":false,"has_abstract":false,"ca_institutions":"University Health Network; Université de Montréal; Montreal Clinical Research Institute; Princess Margaret Cancer Centre","funders":"National Cancer Institute","keywords":"Pharmacogenomics; Computational biology; Biology; Bioinformatics","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.05924038,0.0006604198,0.001490937,0.006980253,0.0009942567,0.003381118,0.002567063,0.001688655,0.001792529],"category_scores_gemma":[0.2983989,0.0009356122,0.00111652,0.00752048,0.002694336,0.004005911,0.002817787,0.001814705,0.0002622726],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001368578,"about_ca_system_score_gemma":0.001451242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001397972,"about_ca_topic_score_gemma":0.00191132,"domain_scores_codex":[0.9483947,0.03227382,0.004369608,0.007353784,0.0070581,0.0005501115],"domain_scores_gemma":[0.4632857,0.4916401,0.01225989,0.02432261,0.007493141,0.0009985387],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002694027,0.0002545926,0.6800136,0.003553221,0.01076938,0.005185653,0.002333421,0.02995328,0.008469768,0.05763223,0.01023722,0.1889037],"study_design_scores_gemma":[0.0004228973,0.0002351577,0.2777778,0.001101549,0.007186669,0.005295336,0.001917889,0.07927345,0.01109618,0.5921872,0.02333796,0.0001679921],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6794294,0.0297405,0.2598591,0.01103344,0.0006059436,0.0003287795,0.008546568,0.001006065,0.00945036],"genre_scores_gemma":[0.9606708,0.001651045,0.03279514,0.001481611,0.0002758886,0.0001082012,0.002640386,0.000148287,0.000228745],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9407596,"threshold_uncertainty_score":0.3132969,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007214287074636669,"score_gpt":0.2729861886712832,"score_spread":0.2657719015966465,"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."}}