{"id":"W4399919623","doi":"10.2196/57670","title":"Novel Approach to Personalized Physician Recommendations Using Semantic Features and Response Metrics: Model Evaluation Study","year":2024,"lang":"en","type":"article","venue":"JMIR Human Factors","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Preprint; Computer science; Information retrieval; World Wide Web; Data science","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":[0.007974633,0.001600251,0.001230115,0.001730384,0.0004193302,0.0009669474,0.001244178,0.001291429,0.001469526],"category_scores_gemma":[0.009836866,0.0003133182,0.001386596,0.001178594,0.0003296435,0.00112954,0.0007077472,0.001468466,0.0003550707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002287327,"about_ca_system_score_gemma":0.00143882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04354253,"about_ca_topic_score_gemma":0.02506674,"domain_scores_codex":[0.9980389,0.001142449,0.0001178941,0.0003161565,0.0002228673,0.0001617106],"domain_scores_gemma":[0.9917049,0.006158278,0.0003600899,0.0003654537,0.001268085,0.0001432502],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001247177,0.001140842,0.02892842,0.0002981908,0.0005726623,0.0001429656,0.0001584685,0.8020369,0.001331832,0.001132196,0.003800452,0.1592098],"study_design_scores_gemma":[0.00001521017,0.0001252407,0.001120718,0.00000587913,0.00003158003,0.00001202701,0.0000126057,0.9982916,0.0001739373,0.0001383519,0.00006731382,0.000005543675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7926739,0.004484407,0.1938083,0.001549373,0.0002174785,0.0004792832,0.001791956,0.001522925,0.003472314],"genre_scores_gemma":[0.954858,0.0004625723,0.04156714,0.0001208857,0.00006229083,0.0002701018,0.001452918,0.00003396271,0.001172207],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04354253,"threshold_uncertainty_score":0.08657819,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1469689032351031,"score_gpt":0.4271232552693717,"score_spread":0.2801543520342686,"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."}}