{"id":"W2955516289","doi":"10.1016/j.jval.2019.04.1395","title":"PNS33 BUGSNET: A NEW COMPREHENSIVE R PACKAGE FOR NETWORK META-ANALYSIS","year":2019,"lang":"en","type":"article","venue":"Value in Health","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary; University of Waterloo","funders":"","keywords":"Computer science; Systematic review; Best practice; Completeness (order theory); Data science; MEDLINE; 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","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.09440537,0.0003880286,0.01111529,0.0007438373,0.0001281147,0.0004533982,0.001603392,0.000106773,0.01449286],"category_scores_gemma":[0.005134954,0.0001939184,0.008575464,0.005215311,0.00002500269,0.0001646985,0.0001147408,0.0001980401,0.003797698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000833775,"about_ca_system_score_gemma":0.0003301366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00165887,"about_ca_topic_score_gemma":0.0003359288,"domain_scores_codex":[0.9683758,0.01391154,0.01156893,0.001565974,0.003848818,0.0007289134],"domain_scores_gemma":[0.9763768,0.0122088,0.005579683,0.004798967,0.0006323935,0.0004033735],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003626836,0.0001127992,0.03364477,0.0004079862,0.0731686,0.000004542013,0.001743198,0.432254,0.000008569315,0.02505898,0.4283618,0.005198528],"study_design_scores_gemma":[0.000675959,0.0002456465,0.01005962,0.00002669745,0.04430673,0.000004697547,0.0006571857,0.1770196,0.000003234077,0.04674356,0.7197587,0.0004984129],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1270568,0.1553507,0.6270527,0.04933438,0.003449403,0.02532181,0.0004530489,0.00007580926,0.01190539],"genre_scores_gemma":[0.7879018,0.0002984965,0.116717,0.01317161,0.0006816767,0.0003190082,0.00009383358,0.00007859358,0.08073792],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.660845,"threshold_uncertainty_score":0.996978,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8786727326830011,"score_gpt":0.5574297508011428,"score_spread":0.3212429818818583,"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."}}