{"id":"W4412941675","doi":"10.2196/77297","title":"Predicting Waiting Times for Medical Tasks in a Pediatric Hospital Using Machine Learning: Comprehensive, Retrospective, Real-World Study","year":2025,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Preprint; Task (project management); Computer science; Medicine; Artificial intelligence; World Wide Web; Engineering; Systems engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00250192,0.0002351727,0.0005701399,0.0005670221,0.001140535,0.00003931753,0.0002908096,0.000467475,0.0003225026],"category_scores_gemma":[0.003959821,0.0002067934,0.00007136638,0.001239829,0.00006655561,0.0002840545,0.0002625268,0.00218834,0.00001430416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004340777,"about_ca_system_score_gemma":0.002011634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009808603,"about_ca_topic_score_gemma":0.001778365,"domain_scores_codex":[0.9954202,0.0005102991,0.002137552,0.0002328822,0.001031253,0.000667783],"domain_scores_gemma":[0.9970405,0.00136479,0.0004544985,0.000241057,0.0005373317,0.0003618318],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003362061,0.0002929306,0.982462,0.0007985395,0.00002658977,0.000007420002,0.01334723,0.0004812363,3.634494e-7,0.001198343,0.0004580866,0.0008936173],"study_design_scores_gemma":[0.002634164,0.000233138,0.07115202,0.0006289469,0.00003240654,0.000001026339,0.0149755,0.9093562,3.74697e-7,0.00005420889,0.0007541943,0.0001778604],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9835849,0.00005212901,0.006957964,0.001752924,0.0007508685,0.003280368,0.00001715477,0.0002059482,0.003397687],"genre_scores_gemma":[0.9896772,0.0001808145,0.007308446,0.001072117,0.0004979565,0.0004894148,0.0001301695,0.00003098209,0.0006129216],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.91131,"threshold_uncertainty_score":0.9507369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04091785846451817,"score_gpt":0.4304763072470105,"score_spread":0.3895584487824923,"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."}}