{"id":"W2001612861","doi":"10.1053/j.ajkd.2009.11.026","title":"Optimal Search Filters for Renal Information in EMBASE","year":2010,"lang":"en","type":"article","venue":"American Journal of Kidney Diseases","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University; Western University","funders":"Canadian Institutes of Health Research; Kidney Foundation of Canada","keywords":"Medicine; MEDLINE; Intensive care medicine; Urology","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":[0.002568464,0.00109347,0.00195835,0.01452902,0.0011069,0.002896753,0.0009823584,0.00168174,0.005279273],"category_scores_gemma":[0.01523748,0.000501889,0.001609133,0.008512522,0.0003455906,0.002659846,0.0009677307,0.0007327076,0.002341947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00129495,"about_ca_system_score_gemma":0.005300585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02078103,"about_ca_topic_score_gemma":0.03163769,"domain_scores_codex":[0.9979825,0.0004599707,0.0003983263,0.0003730994,0.000539205,0.000246945],"domain_scores_gemma":[0.9927602,0.005110174,0.0003976603,0.0003563,0.001219998,0.0001556815],"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":[0.003364274,0.0006465801,0.02131827,0.003579087,0.0007588262,0.001350769,0.0007065652,0.02694198,0.03027201,0.0125913,0.0712324,0.827238],"study_design_scores_gemma":[0.0008199112,0.001147721,0.04810108,0.001886243,0.003027998,0.003036468,0.00306615,0.7157128,0.08049984,0.05692399,0.08551931,0.0002584329],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3735139,0.02232942,0.5062377,0.004072093,0.0006245829,0.001317255,0.06731335,0.01276179,0.01182979],"genre_scores_gemma":[0.4287691,0.004157729,0.4956751,0.0004287294,0.0002493239,0.0005126194,0.06272103,0.0006120944,0.006874275],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9974315,"threshold_uncertainty_score":0.04132015,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006314968887233484,"score_gpt":0.2740617849546118,"score_spread":0.2677468160673783,"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."}}