{"id":"W4324019806","doi":"10.1186/s12874-023-01884-x","title":"Measuring the impact of zero-cases studies in evidence synthesis practice using the harms index and benefits index (Hi-Bi)","year":2023,"lang":"en","type":"article","venue":"BMC Medical Research Methodology","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Medical Research Council; Anhui Medical University; National Health and Medical Research Council; National Natural Science Foundation of China","keywords":"Meta-analysis; Index (typography); Measure (data warehouse); Computer science; Statistics; Medicine; Sensitivity (control systems); Systematic review; MEDLINE; Mathematics; Data mining; Pathology","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":[0.5482633,0.004675088,0.009609334,0.0208155,0.002620843,0.01210529,0.005829895,0.006282292,0.005910189],"category_scores_gemma":[0.7991052,0.002956189,0.02079011,0.0144892,0.008581221,0.0103573,0.01159315,0.007958482,0.0008644216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006108101,"about_ca_system_score_gemma":0.01096037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002008336,"about_ca_topic_score_gemma":0.002392452,"domain_scores_codex":[0.2696467,0.5238322,0.1075704,0.02106002,0.07650127,0.001389495],"domain_scores_gemma":[0.1068801,0.8103819,0.03979924,0.02740417,0.01439918,0.001135533],"domain_codex":"methods","domain_gemma":"methods","domain_candidate":"methods","domain_consensus":"methods","study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.005401044,0.0005493797,0.09211109,0.1751243,0.1378747,0.0009633633,0.006101009,0.02229328,0.003222497,0.07545985,0.01337544,0.467524],"study_design_scores_gemma":[0.006116301,0.004779582,0.06252296,0.08396894,0.1717735,0.002568911,0.002979737,0.07540751,0.01480867,0.5068637,0.06663414,0.001576071],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02273848,0.09195025,0.8406473,0.011541,0.003348417,0.01237702,0.003745025,0.001094299,0.01255813],"genre_scores_gemma":[0.3190863,0.01010264,0.6444007,0.004693809,0.0009756325,0.01845266,0.001227478,0.0003155785,0.0007451532],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4517367,"threshold_uncertainty_score":0.5570719,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9903611485543717,"score_gpt":0.7623216590357637,"score_spread":0.2280394895186081,"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."}}