{"id":"W2169217275","doi":"10.1111/j.1365-2923.2010.03705.x","title":"How clinical features are presented matters to weaker diagnosticians","year":2010,"lang":"en","type":"article","venue":"Medical Education","topic":"Clinical Reasoning and Diagnostic Skills","field":"Medicine","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Medical Council of Canada; University of British Columbia","funders":"","keywords":"Terminology; Medical diagnosis; Test (biology); Context (archaeology); Medical terminology; Psychology; Unified Medical Language System; Language assessment; Sample (material); Medical education; Medicine; Family medicine; Linguistics; Computer science; Artificial intelligence; Nursing; Mathematics education; Radiology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0006690475,0.0001558615,0.0003645861,0.00008983652,0.00006084429,0.00005985768,0.0001793264,0.0003815832,0.0007981653],"category_scores_gemma":[0.3839744,0.000119845,0.0001500521,0.0002322265,0.0001922313,0.00004463679,0.00005767785,0.0009724948,0.0003107451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002553355,"about_ca_system_score_gemma":0.0009750303,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001334039,"about_ca_topic_score_gemma":0.0002249323,"domain_scores_codex":[0.9980724,0.00008187216,0.0003894638,0.000417059,0.000729285,0.0003099569],"domain_scores_gemma":[0.9888885,0.00869815,0.000133922,0.0006060204,0.0002735718,0.001399844],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0000525089,0.001269533,0.1769358,0.00001494072,0.00002229928,0.0000256545,0.00007654943,9.900272e-8,0.00003676189,0.0001428671,0.7305755,0.09084742],"study_design_scores_gemma":[0.0007160886,0.0001614227,0.7283203,0.0009652087,0.00008510652,0.00006883911,0.0002118217,0.00002410881,0.00007610648,0.00009029559,0.2691567,0.0001240599],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.4777263,0.00007061328,0.0001961487,0.5165722,0.004233668,0.0003433056,0.000004281742,0.0000809641,0.000772552],"genre_scores_gemma":[0.8228402,0.00008314413,0.001896146,0.1632621,0.004032953,0.0001298329,0.0001073463,0.00003549309,0.007612826],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5513844,"threshold_uncertainty_score":0.873935,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0156917389646124,"score_gpt":0.3921015967209331,"score_spread":0.3764098577563207,"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."}}