{"id":"W3186848610","doi":"10.1016/j.jclinepi.2021.07.011","title":"GRADE guidelines 33: Addressing imprecision in a network meta-analysis","year":2021,"lang":"en","type":"article","venue":"Journal of Clinical Epidemiology","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":89,"is_retracted":false,"has_abstract":false,"ca_institutions":"University Health Network; Impact; McMaster University","funders":"","keywords":"Certainty; Confidence interval; Context (archaeology); Computer science; Meta-analysis; Interval (graph theory); Statistics; Medicine; Mathematics","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","metaepi_broad","insufficient_payload"],"consensus_categories":["metaresearch","metaepi_broad"],"category_scores_codex":[0.7981485,0.0003955684,0.04511955,0.0007913511,0.00009213722,0.0002023002,0.002332107,0.0004981391,0.01466761],"category_scores_gemma":[0.887246,0.0001649191,0.04265125,0.004701137,0.0001597904,0.0002817218,0.0003285691,0.001265853,0.0003449217],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002634289,"about_ca_system_score_gemma":0.0003641587,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001617129,"about_ca_topic_score_gemma":0.0002292137,"domain_scores_codex":[0.4472292,0.3673033,0.1762019,0.002133417,0.006170241,0.0009619233],"domain_scores_gemma":[0.2975135,0.5874042,0.09787003,0.006173685,0.01008916,0.0009494809],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005921432,0.0002163713,0.4589712,0.00001391342,0.08801247,0.0002611776,0.00004145549,0.07972006,0.000005657437,0.002120057,0.3341627,0.03641577],"study_design_scores_gemma":[0.0007365469,0.0001887108,0.2277041,0.00005075311,0.1551058,0.0002349992,0.0001995575,0.05908228,0.00000263336,0.2273469,0.3290133,0.0003344386],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1269552,0.1115245,0.5970998,0.155594,0.005330973,0.0005513729,0.00003080658,0.000007612818,0.002905756],"genre_scores_gemma":[0.4116165,0.003221362,0.544579,0.02901537,0.003756713,0.00001426565,0.00001117786,0.00003331529,0.00775228],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2846613,"threshold_uncertainty_score":0.9862331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9880881395648715,"score_gpt":0.7575602546365781,"score_spread":0.2305278849282935,"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."}}