{"id":"W4230570618","doi":"10.21203/rs.2.14912/v1","title":"Database combinations to retrieve systematic reviews in Overviews of reviews: A methodological study","year":2019,"lang":"en","type":"preprint","venue":"Research Square","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Systematic review; Information retrieval; Computer science; Database; Data science; Political science; MEDLINE","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["metaresearch"],"domain":"methods","study_design":"simulation_or_modeling","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":["metaresearch"],"domain":"methods","study_design":"design_other","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"medium","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.3062807,0.004553294,0.01369893,0.05801762,0.002507064,0.0117705,0.004074125,0.003626219,0.008612184],"category_scores_gemma":[0.6606857,0.004982861,0.02378075,0.07396866,0.002103602,0.01419927,0.01075518,0.002858163,0.001926943],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006498889,"about_ca_system_score_gemma":0.01242971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001820131,"about_ca_topic_score_gemma":0.003076047,"domain_scores_codex":[0.3600555,0.3907011,0.1896259,0.0121159,0.04571483,0.001786814],"domain_scores_gemma":[0.2021077,0.6732171,0.05459389,0.02878179,0.03942166,0.001877877],"domain_codex":"methods","domain_gemma":"methods","domain_candidate":"methods","domain_consensus":"methods","study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.01539458,0.001372482,0.04633756,0.3582515,0.07123709,0.0006998964,0.007132335,0.005429796,0.003386538,0.006494874,0.004470648,0.4797927],"study_design_scores_gemma":[0.03531931,0.024243,0.07662894,0.2921773,0.4239167,0.004777172,0.01006173,0.03118286,0.01015623,0.0288622,0.06058988,0.002084581],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"review","genre_gemma":"methods","genre_scores_codex":[0.1533373,0.4454919,0.1967319,0.003886586,0.001160097,0.1696736,0.01525261,0.001450357,0.01301578],"genre_scores_gemma":[0.257594,0.07343176,0.5097638,0.001152915,0.0004998143,0.1511109,0.005545933,0.0002977942,0.0006030402],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6937193,"threshold_uncertainty_score":0.8554794,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9715803719910722,"score_gpt":0.7239185855539376,"score_spread":0.2476617864371345,"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."}}