{"id":"W2047221535","doi":"10.1016/j.jclinepi.2011.10.014","title":"Search filters can find some but not all knowledge translation articles in MEDLINE: an analytic survey","year":2012,"lang":"en","type":"review","venue":"Journal of Clinical Epidemiology","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":41,"is_retracted":false,"has_abstract":false,"ca_institutions":"St. Michael's Hospital; University of Toronto; McMaster University","funders":"Canadian Institutes of Health Research","keywords":"Terminology; MEDLINE; Filter (signal processing); Information retrieval; Computer science; Field (mathematics); Sensitivity (control systems); Translation (biology); Medical physics; Medicine; Mathematics; Linguistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01518774,0.00101182,0.002815678,0.04377338,0.0008236608,0.002895966,0.001328777,0.001430216,0.004660212],"category_scores_gemma":[0.09838264,0.0005187935,0.002646322,0.04915055,0.0006629393,0.004603708,0.001551328,0.0006240413,0.001390987],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001288333,"about_ca_system_score_gemma":0.005077085,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003834569,"about_ca_topic_score_gemma":0.0142668,"domain_scores_codex":[0.9893942,0.002353548,0.004263512,0.0008287184,0.002813144,0.0003468696],"domain_scores_gemma":[0.7953234,0.1715482,0.01673732,0.00244697,0.01293741,0.001006693],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001502162,0.0001670182,0.06604442,0.1616299,0.007402988,0.001157293,0.003075756,0.0003956343,0.003147872,0.00251267,0.03730013,0.7156641],"study_design_scores_gemma":[0.0006520275,0.0008285746,0.2955619,0.1944631,0.06876536,0.01072978,0.006143783,0.001379472,0.006454944,0.008674508,0.4060765,0.0002702158],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.05649422,0.9028265,0.003589213,0.005090807,0.0001484198,0.0004453476,0.02504976,0.0002249709,0.006130653],"genre_scores_gemma":[0.1936701,0.7504391,0.01744226,0.004154527,0.0003243323,0.0007480856,0.03084596,0.0001893725,0.002186277],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9848123,"threshold_uncertainty_score":0.08032143,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.727354163457325,"score_gpt":0.5872265397795597,"score_spread":0.1401276236777652,"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."}}