{"id":"W3104946968","doi":"10.1177/0141076820956799","title":"Reducing bias and improving transparency in medical research: a critical overview of the problems, progress and suggested next steps","year":2020,"lang":"en","type":"review","venue":"Journal of the Royal Society of Medicine","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":93,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Institute for Health and Care Research; Cancer Research UK","keywords":"Transparency (behavior); Documentation; Medical research; Computer science; Public relations; Data science; Public opinion; Political science; Medicine; Computer security; Law","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":["metaresearch"],"category_scores_codex":[0.2466282,0.001853446,0.005725562,0.01650084,0.003540769,0.01584961,0.003848245,0.01058426,0.003904729],"category_scores_gemma":[0.2580279,0.00204684,0.004340306,0.01699833,0.01338059,0.02611752,0.007118304,0.01886457,0.001220431],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01357284,"about_ca_system_score_gemma":0.04395064,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00561167,"about_ca_topic_score_gemma":0.0109517,"domain_scores_codex":[0.8560519,0.09598568,0.02105809,0.00375829,0.02083675,0.0023094],"domain_scores_gemma":[0.4316456,0.5078048,0.01813801,0.006418241,0.033213,0.002780421],"domain_codex":null,"domain_gemma":"reproducibility","domain_candidate":"reproducibility","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000161324,0.0001044395,0.0009717453,0.1406627,0.0008740234,0.0002746546,0.004339797,0.0008037784,0.0004735181,0.08551417,0.03448535,0.7313344],"study_design_scores_gemma":[0.00008462196,0.0003245912,0.002458729,0.321444,0.001177664,0.0008505875,0.004247751,0.0008881902,0.0006215599,0.1166918,0.550953,0.0002575143],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0001130954,0.9497548,0.002188666,0.04598764,0.001301614,0.00007702343,0.0000159558,0.00002114294,0.0005400004],"genre_scores_gemma":[0.003270593,0.9743314,0.01072768,0.0092025,0.001962459,0.0002478524,0.00002807841,0.00001827302,0.0002111657],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.7533718,"threshold_uncertainty_score":0.9290416,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6953636628146425,"score_gpt":0.5333599919703002,"score_spread":0.1620036708443423,"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."}}