{"id":"W2082134571","doi":"10.3138/jvme.33.3.474","title":"Learning Evidence-Based Veterinary Medicine through Development of a Critically Appraised Topic","year":2006,"lang":"en","type":"article","venue":"Journal of Veterinary Medical Education","topic":"Health Sciences Research and Education","field":"Health Professions","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Human medicine; Informatics; Veterinary medicine; Medical education; Alternative medicine; Evidence-based medicine; Medicine; Class (philosophy); Psychology; Computer science; Pathology; Political science; Traditional medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004697392,0.0001226912,0.0003455048,0.000257153,0.0004168578,0.000006659094,0.0003641534,0.0001717367,0.002132581],"category_scores_gemma":[0.01283326,0.0000912233,0.00005813732,0.000414734,0.0002540993,0.0004219953,0.00005377258,0.0008754364,0.00002529938],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003305648,"about_ca_system_score_gemma":0.01723872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002281139,"about_ca_topic_score_gemma":0.00001107622,"domain_scores_codex":[0.9953241,0.001039632,0.001591115,0.000170202,0.001421251,0.0004536581],"domain_scores_gemma":[0.9957311,0.001798588,0.0006659651,0.0001577342,0.001179057,0.0004675664],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.005000681,0.007131306,0.1668541,0.01802432,0.0001054813,0.0002629948,0.07291777,0.0001202668,0.07687991,0.003181159,0.2035647,0.4459572],"study_design_scores_gemma":[0.002799542,0.0115019,0.4869264,0.01756608,0.00004303738,0.0002591344,0.01801767,0.0008660965,0.000283579,0.001249673,0.4601347,0.0003521037],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.965662,0.001016806,0.004505823,0.02422621,0.002640633,0.0002630578,2.663831e-7,0.00001352103,0.001671669],"genre_scores_gemma":[0.9826013,0.0001386669,0.01313709,0.001798164,0.001908771,0.00004607687,0.000008522785,0.00001059571,0.0003508026],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4456051,"threshold_uncertainty_score":0.9987796,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3800567010596598,"score_gpt":0.5772942307137011,"score_spread":0.1972375296540413,"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."}}