{"id":"W2062866473","doi":"10.1503/cmaj.1041620","title":"Case summaries: another method","year":2005,"lang":"en","type":"letter","venue":"Canadian Medical Association Journal","topic":"Empathy and Medical Education","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Baycrest Hospital","funders":"","keywords":"Salient; Computer science; Information retrieval; Data science; Natural language processing; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.008386863,0.001429262,0.0006578979,0.01167943,0.00247307,0.005618259,0.002389984,0.004627405,0.02191062],"category_scores_gemma":[0.04470554,0.0005480431,0.001010899,0.003507026,0.003672118,0.009033887,0.005202338,0.006137124,0.007079823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001829621,"about_ca_system_score_gemma":0.002110766,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009340522,"about_ca_topic_score_gemma":0.001831283,"domain_scores_codex":[0.9872395,0.007742677,0.001171546,0.0008746093,0.002484703,0.0004868788],"domain_scores_gemma":[0.9717116,0.016056,0.002122822,0.003908485,0.00461649,0.001584527],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002776822,0.0001770155,0.006197413,0.001177151,0.00007389683,0.01504399,0.003122572,0.0002801679,0.001067602,0.1245438,0.4546409,0.3933979],"study_design_scores_gemma":[0.00018722,0.0001440565,0.001734879,0.002551086,0.00009319612,0.1544488,0.003022908,0.001169464,0.001201552,0.0733998,0.7618775,0.0001694775],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.01443838,0.05599486,0.3183607,0.3402643,0.05631084,0.002413813,0.001383671,0.002498095,0.2083353],"genre_scores_gemma":[0.2949039,0.06606014,0.3750823,0.1080773,0.05040157,0.004175455,0.001049858,0.00148051,0.09876903],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.02191062,"threshold_uncertainty_score":0.07329828,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01813808732461052,"score_gpt":0.302280004848365,"score_spread":0.2841419175237545,"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."}}