{"id":"W3010095828","doi":"10.1097/ccm.0000000000004246","title":"Development and Reporting of Prediction Models: Guidance for Authors From Editors of Respiratory, Sleep, and Critical Care Journals","year":2020,"lang":"en","type":"article","venue":"Critical Care Medicine","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":284,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"National Heart, Lung, and Blood Institute; National Institute for Health and Care Research; U.S. Department of Veterans Affairs","keywords":"Operationalization; Medicine; Predictive modelling; Best practice; Inference; Causal inference; Casual; Missing data; MEDLINE; Set (abstract data type); Actuarial science; Computer science; Artificial intelligence; Machine learning","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":[0.4604539,0.004880053,0.0111568,0.02287365,0.005576164,0.02801428,0.01376469,0.01640526,0.05763985],"category_scores_gemma":[0.8568528,0.007674567,0.01088908,0.02552373,0.007263101,0.02762642,0.01408995,0.02043235,0.08205254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006979354,"about_ca_system_score_gemma":0.06972501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004950811,"about_ca_topic_score_gemma":0.007363138,"domain_scores_codex":[0.4475374,0.2914986,0.1924694,0.01186158,0.05288375,0.003749298],"domain_scores_gemma":[0.06547755,0.4085653,0.09482463,0.05637171,0.3667389,0.008021804],"domain_codex":null,"domain_gemma":"reporting","domain_candidate":"reporting","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001619843,0.00005455697,0.0005068468,0.005390229,0.0001385857,0.00008196582,0.0009240081,0.0003896719,0.00017839,0.002659764,0.9281012,0.06141276],"study_design_scores_gemma":[0.0009410481,0.0001543522,0.001391182,0.03594177,0.0004610778,0.0002970261,0.001631293,0.003336509,0.00101774,0.03268549,0.9217302,0.0004122949],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"methods","genre_scores_codex":[0.001255234,0.02475351,0.2822569,0.4637336,0.1235723,0.0334614,0.03176966,0.01760867,0.02158877],"genre_scores_gemma":[0.006615364,0.02644871,0.746501,0.07533935,0.04232383,0.06752775,0.01404727,0.007023258,0.01417335],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5395461,"threshold_uncertainty_score":0.6653564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7475708684647632,"score_gpt":0.552404215678449,"score_spread":0.1951666527863142,"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."}}