{"id":"W2046657252","doi":"10.1111/j.1547-5069.2006.00100.x","title":"Optimal CINAHL Search Strategies for Identifying Therapy Studies and Review Articles","year":2006,"lang":"en","type":"article","venue":"Journal of Nursing Scholarship","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":85,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Toronto Metropolitan University","funders":"U.S. National Library of Medicine","keywords":"CINAHL; Sensitivity (control systems); MEDLINE; Gold standard (test); Computer science; Term (time); Search engine indexing; Medicine; Systematic review; Medical physics; Information retrieval; Data mining; Internal medicine; Engineering","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":[0.2325252,0.004534954,0.009477052,0.09476054,0.003468885,0.01512946,0.005384437,0.004414388,0.008023542],"category_scores_gemma":[0.6552873,0.004049402,0.007959317,0.06104896,0.0022193,0.01428679,0.008598593,0.00182748,0.002308816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01734454,"about_ca_system_score_gemma":0.04297224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009011077,"about_ca_topic_score_gemma":0.01770901,"domain_scores_codex":[0.6389952,0.2139741,0.1068176,0.009741097,0.0267803,0.003691645],"domain_scores_gemma":[0.4090689,0.4702323,0.05453287,0.01051571,0.05209075,0.003559486],"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":[0.01327832,0.001024057,0.03955036,0.2627518,0.007492125,0.001000784,0.009736797,0.007850047,0.004018942,0.01183498,0.01175139,0.6297104],"study_design_scores_gemma":[0.0369943,0.01375906,0.1068974,0.440038,0.08942656,0.004139727,0.04321927,0.03911879,0.01542056,0.07075385,0.1374251,0.00280735],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"protocol","genre_gemma":"empirical","genre_scores_codex":[0.2031592,0.2342646,0.1791687,0.01067524,0.001193724,0.3210337,0.01952188,0.002762237,0.02822064],"genre_scores_gemma":[0.2005825,0.04017381,0.5703606,0.001776935,0.0002643041,0.1802544,0.005068904,0.0002463336,0.001272256],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7674748,"threshold_uncertainty_score":0.9464331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8977447082458753,"score_gpt":0.6343550422678338,"score_spread":0.2633896659780415,"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."}}