{"id":"W2991698744","doi":"10.36834/cmej.36694","title":"Effects of a patient’s name and image on medical knowledge acquisition","year":2015,"lang":"en","type":"article","venue":"Canadian Medical Education Journal","topic":"Patient-Provider Communication in Healthcare","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Knowledge acquisition; Image (mathematics); Medical knowledge; Information retrieval; Artificial intelligence; Medicine; Medical education","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009249274,0.0001614959,0.000231459,0.0001297358,0.0001701334,0.0003188073,0.0002103816,0.0003165555,0.005418519],"category_scores_gemma":[0.00813346,0.00009510633,0.0001988725,0.00006326358,0.0002852263,0.000414487,0.0003983784,0.0003584544,0.0003350344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002075995,"about_ca_system_score_gemma":0.0002742757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006150033,"about_ca_topic_score_gemma":0.0008411731,"domain_scores_codex":[0.9991189,0.0004523549,0.00005495198,0.00009180694,0.0002113963,0.00007061499],"domain_scores_gemma":[0.995008,0.003209308,0.0009475716,0.0001722773,0.0001714847,0.0004913291],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.03943029,0.02275368,0.5146219,0.0007591953,0.0003681133,0.001427918,0.009427125,0.0009503681,0.0765688,0.0003187931,0.001445234,0.3319286],"study_design_scores_gemma":[0.0003204677,0.03082513,0.9515881,0.00007487813,0.0001454491,0.001173454,0.00224415,0.0007383349,0.01114045,0.0001314647,0.001592248,0.00002585558],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9994546,0.00005347684,0.00004425642,0.00002661669,0.000003888225,0.000007190843,0.000006388676,0.00000262127,0.0004008889],"genre_scores_gemma":[0.9991432,0.00005538986,0.0002361857,0.00002099617,0.000004227252,0.00001447326,0.0000204566,0.000001100963,0.0005039579],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005418519,"threshold_uncertainty_score":0.01812673,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05994501501201235,"score_gpt":0.4150559513627362,"score_spread":0.3551109363507238,"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."}}