{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009346747,0.0008871813,0.0006275164,0.001791786,0.0003366476,0.001053613,0.0008759162,0.0005662515,0.0006803698],"category_scores_gemma":[0.01426395,0.0003857386,0.001358875,0.0008776049,0.0003799376,0.0005767695,0.0007632468,0.001178557,0.0001850898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001010092,"about_ca_system_score_gemma":0.001266487,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007569396,"about_ca_topic_score_gemma":0.004765368,"domain_scores_codex":[0.9981307,0.001004127,0.000190872,0.0003237742,0.0002111824,0.0001393541],"domain_scores_gemma":[0.9876928,0.009288686,0.001016134,0.0008163857,0.0009104841,0.0002755009],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006576742,0.0003345897,0.7428496,0.00007231964,0.0005855129,0.000291666,0.0001625667,0.2176532,0.0005459423,0.000409626,0.00102944,0.03540788],"study_design_scores_gemma":[0.0000228748,0.0003363349,0.06123946,0.00003081446,0.000128396,0.0002106065,0.0000926419,0.9359292,0.0009831097,0.0006846465,0.0003176581,0.00002423048],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9749211,0.0003940519,0.0229139,0.00024479,0.00001861823,0.0001014625,0.0009803294,0.0001186891,0.0003071348],"genre_scores_gemma":[0.9901071,0.0001239078,0.00808309,0.0000231829,0.00001079198,0.00005846825,0.001488169,0.00000738177,0.00009794308],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009346747,"threshold_uncertainty_score":0.04943097,"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."}}