{"id":"W3092643853","doi":"10.2196/21252","title":"Patient Triage by Topic Modeling of Referral Letters: Feasibility Study","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Healthcare Systems and Technology","field":"Business, Management and Accounting","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Health and Care Research Wales","keywords":"Triage; Referral; Topic model; Latent Dirichlet allocation; Artificial intelligence; Context (archaeology); Computer science; Set (abstract data type); Relevance (law); Medicine; Machine learning; Family medicine; Medical emergency","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":[0.009813963,0.0007905169,0.0008238013,0.0008627549,0.0005799751,0.001235297,0.001436905,0.001868806,0.00333041],"category_scores_gemma":[0.04338899,0.0004907537,0.001267925,0.0006445945,0.0005970041,0.001350414,0.00126392,0.001375613,0.001348762],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001373795,"about_ca_system_score_gemma":0.002210868,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00613503,"about_ca_topic_score_gemma":0.003097817,"domain_scores_codex":[0.9936677,0.004777919,0.0002871682,0.0006421393,0.0004022308,0.0002228682],"domain_scores_gemma":[0.94832,0.04517829,0.00129922,0.001799823,0.00243815,0.0009645451],"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":[0.01816748,0.01389773,0.139988,0.001583175,0.0005184341,0.002682819,0.007743134,0.4176645,0.01331322,0.004053863,0.009326614,0.371061],"study_design_scores_gemma":[0.0006504911,0.002154132,0.01002689,0.00004594979,0.00007436639,0.0003173349,0.0008069975,0.9806747,0.001882669,0.0020918,0.00122056,0.0000541332],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8877356,0.0002066572,0.1053863,0.001107837,0.00008552474,0.002166882,0.0006757771,0.001124991,0.001510385],"genre_scores_gemma":[0.9376549,0.00009060757,0.0601397,0.0001472476,0.00006535281,0.0009011341,0.0005447002,0.00004265139,0.0004137265],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009813963,"threshold_uncertainty_score":0.05190182,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06571533772948462,"score_gpt":0.310388216746979,"score_spread":0.2446728790174944,"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."}}