{"id":"W2115003496","doi":"","title":"Estudio aleatorio de tiempos de espera de pacientes según niveles de prioridad","year":2004,"lang":"es","type":"dissertation","venue":"Universidad Nacional Mayor de San Marcos. Programa Cybertesis PERÚ","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Seriousness; Percentile; Humanities; Triage; Waiting list; Medical emergency; Statistics; Internal medicine; Political science; Mathematics; Art","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0007002566,0.0001595468,0.0002807057,0.0005777992,0.0002703286,0.0004070796,0.0001565186,0.0003474168,0.002429817],"category_scores_gemma":[0.005549012,0.0002194757,0.0004153092,0.0005131774,0.0001595502,0.000431152,0.0003004023,0.0004056327,0.0003215169],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002127017,"about_ca_system_score_gemma":0.0003299614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002205524,"about_ca_topic_score_gemma":0.003041597,"domain_scores_codex":[0.9997012,0.0001010133,0.00003884108,0.00004764576,0.0000429505,0.00006832821],"domain_scores_gemma":[0.997857,0.000895105,0.0008138234,0.0001039493,0.0001780566,0.0001519026],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008975648,0.0001548614,0.9906343,0.00005005111,0.00007284027,0.0001632562,0.0005109819,0.00003538786,0.0002079688,0.00002782469,0.0002055643,0.007039449],"study_design_scores_gemma":[0.00004779785,0.001116887,0.9958216,0.0000306828,0.00009458281,0.0005290041,0.001176113,0.0003103195,0.0001237866,0.00009242185,0.0006475205,0.000009349457],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9991447,0.000257749,0.00008031866,0.00003888556,0.000003306139,0.00001182805,0.0001128616,0.000002086143,0.0003483238],"genre_scores_gemma":[0.9989988,0.0002896485,0.0001341909,0.00003495729,0.000009595221,0.00003149118,0.0001724708,0.00000171288,0.0003270621],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002429817,"threshold_uncertainty_score":0.008128524,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01921282067218365,"score_gpt":0.3412575482691731,"score_spread":0.3220447275969895,"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."}}