{"id":"W4408127928","doi":"10.2196/57719","title":"Predicting Escalation of Care for Childhood Pneumonia Using Machine Learning: Retrospective Analysis and Model Development","year":2025,"lang":"en","type":"article","venue":"JMIRx Med","topic":"Pneumonia and Respiratory Infections","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pneumonia; De-escalation; Medicine; Computer science; Intensive care medicine; Internal medicine","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":[0.0001168369,0.0001006111,0.0003195851,0.0003640218,0.0002223635,0.000008451231,0.00002508869,0.00008008124,0.000003411638],"category_scores_gemma":[0.0001754908,0.00008822101,0.00009891994,0.0005187073,0.0000288165,0.00003875716,0.00002695294,0.0001403276,1.464473e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001360797,"about_ca_system_score_gemma":0.0001668863,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002087702,"about_ca_topic_score_gemma":0.0001327536,"domain_scores_codex":[0.9992753,0.00002154074,0.0002527021,0.0002014684,0.0001254712,0.0001235474],"domain_scores_gemma":[0.9994804,0.00003917194,0.0001095541,0.0001039399,0.0002155577,0.00005136458],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002720365,0.00006337333,0.9460441,0.0001411728,0.0005541762,5.052015e-7,0.002087374,0.004198018,0.04578576,0.00004431999,0.000001986662,0.001051966],"study_design_scores_gemma":[0.0008230275,0.0000994839,0.7603223,0.00009879883,0.001010013,0.000002161215,0.0002837376,0.2294098,0.007587297,0.00003645985,0.0002521636,0.00007471341],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9836416,0.000112336,0.01505971,0.0002255623,0.000039755,0.0004648137,0.00001128439,0.00004814837,0.00039681],"genre_scores_gemma":[0.9973276,0.000007058848,0.002070328,0.00004326661,0.00002059183,0.00003759226,0.00003742219,0.000009689013,0.0004464435],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2252118,"threshold_uncertainty_score":0.3597548,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01078925169670638,"score_gpt":0.2907657125852809,"score_spread":0.2799764608885745,"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."}}