{"id":"W2022276473","doi":"10.1037/0278-7393.30.3.563","title":"Using Comprehensive Feature Lists to Bias Medical Diagnosis.","year":2004,"lang":"en","type":"article","venue":"Journal of Experimental Psychology Learning Memory and Cognition","topic":"Empathy and Medical Education","field":"Medicine","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Feature (linguistics); Diagnostic accuracy; Medicine; Medical literature; Medical physics; Psychology; Computer science; Radiology; Pathology; Linguistics","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":[],"consensus_categories":[],"category_scores_codex":[0.07577585,0.0008043292,0.0007589083,0.003575648,0.0014584,0.002432453,0.001126816,0.001949819,0.005808703],"category_scores_gemma":[0.4533755,0.0006731594,0.0006607965,0.001928049,0.002621534,0.006540544,0.003634511,0.001462652,0.001124981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001996743,"about_ca_system_score_gemma":0.001812246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001551542,"about_ca_topic_score_gemma":0.003806703,"domain_scores_codex":[0.9199612,0.05316884,0.009574817,0.00391753,0.01246076,0.0009168529],"domain_scores_gemma":[0.4074033,0.4609904,0.08265617,0.02607551,0.0202695,0.002605075],"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":[0.009130345,0.001017611,0.2868874,0.004090943,0.001019851,0.0006999213,0.01939583,0.001514635,0.01170607,0.02066045,0.03873178,0.6051452],"study_design_scores_gemma":[0.002901287,0.008513934,0.5663818,0.009865219,0.002802274,0.009498885,0.01640321,0.01988832,0.05387002,0.1362138,0.1723604,0.001300859],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7665461,0.01087928,0.1363546,0.02229161,0.00352994,0.003673672,0.002162313,0.001478013,0.0530844],"genre_scores_gemma":[0.9125282,0.001391559,0.07101179,0.008609868,0.0007977959,0.001599098,0.0007332477,0.0001313478,0.00319706],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07577585,"threshold_uncertainty_score":0.4007458,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09710683638292317,"score_gpt":0.4308246209643348,"score_spread":0.3337177845814116,"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."}}