{"id":"W3020987287","doi":"10.29173/jchla29437","title":"Can database-level MEDLINE exclusion filters in Embase and CINAHL be used to remove duplicate records without loss of relevant studies in systematic reviews? An exploratory study","year":2020,"lang":"en","type":"article","venue":"Journal of the Canadian Health Libraries Association / Journal de l Association de bilbiothèques de la santé du Canada","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"CINAHL; MEDLINE; Medicine; Information retrieval; Computer science; Chemistry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.6649868,0.003319992,0.00965981,0.02795842,0.004397576,0.01562334,0.008322907,0.008818129,0.009904655],"category_scores_gemma":[0.8969364,0.003598006,0.0158104,0.04237712,0.005014444,0.02646732,0.0109887,0.003348499,0.002101375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01339963,"about_ca_system_score_gemma":0.03213226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006520834,"about_ca_topic_score_gemma":0.009624826,"domain_scores_codex":[0.2353061,0.5070024,0.1607283,0.01577448,0.07540525,0.005783413],"domain_scores_gemma":[0.07002467,0.7735634,0.08671938,0.03160765,0.0371803,0.0009046514],"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":[0.0149099,0.001751125,0.06437185,0.339178,0.03403191,0.001662663,0.04181344,0.001839344,0.00317599,0.01415219,0.02201645,0.4610971],"study_design_scores_gemma":[0.01945788,0.0171519,0.1771168,0.3776017,0.09911855,0.004714068,0.03686308,0.01176185,0.01839924,0.04020888,0.1955881,0.002018022],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.2801817,0.156631,0.2607176,0.05263771,0.004609423,0.197588,0.01694565,0.002558242,0.02813071],"genre_scores_gemma":[0.4002867,0.0217242,0.353211,0.02000291,0.001351169,0.1960209,0.004860084,0.0005579529,0.001985003],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3350132,"threshold_uncertainty_score":0.4131309,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3936028617850207,"score_gpt":0.4461690169513446,"score_spread":0.0525661551663239,"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."}}