{"id":"W2981452012","doi":"10.1186/s12874-019-0829-2","title":"BUGSnet: an R package to facilitate the conduct and reporting of Bayesian network Meta-analyses","year":2019,"lang":"en","type":"article","venue":"BMC Medical Research Methodology","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":319,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; Public Health Ontario; University of Waterloo","funders":"University of Waterloo","keywords":"Computer science; Bayesian probability; Nice; Inference; Bayesian network; Gibbs sampling; Data mining; Data science; Machine learning; Operations research; Artificial intelligence; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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","insufficient_payload"],"consensus_categories":["metaresearch","insufficient_payload"],"category_scores_codex":[0.8535877,0.0003034631,0.008746522,0.0005479185,0.0002333893,0.0002821023,0.003228605,0.0002394635,0.04568693],"category_scores_gemma":[0.8736935,0.0001059734,0.001982065,0.002832173,0.0007281927,0.0001513272,0.0008943671,0.0007870075,0.0009043295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001895679,"about_ca_system_score_gemma":0.000597025,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004823291,"about_ca_topic_score_gemma":0.001402961,"domain_scores_codex":[0.3427695,0.5956274,0.03091038,0.003123395,0.02592685,0.00164248],"domain_scores_gemma":[0.3074303,0.6575924,0.01434377,0.01301906,0.005401393,0.002213095],"domain_codex":"methods","domain_gemma":"methods","domain_candidate":"methods","domain_consensus":"methods","study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006698034,0.0003543627,0.2309749,0.001392444,0.01890395,0.0002171597,0.01488528,0.002977142,0.01388507,0.06421934,0.3188866,0.3326339],"study_design_scores_gemma":[0.001350477,0.003189131,0.08497331,0.0002267761,0.007223051,0.0004407148,0.03402036,0.06188311,0.001414442,0.3134416,0.4905662,0.001270856],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3690816,0.005618363,0.6097715,0.006320025,0.0003733038,0.002163044,0.00001558989,0.000008703097,0.006647803],"genre_scores_gemma":[0.6383692,0.0001076775,0.338438,0.001231465,0.0003124656,0.0001947406,0.000008182651,0.0000292553,0.02130902],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3313631,"threshold_uncertainty_score":0.9998736,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9946169984997515,"score_gpt":0.76499760368872,"score_spread":0.2296193948110314,"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."}}