{"id":"W4408127938","doi":"10.2196/71098","title":"Authors’ Response to Peer Reviews of “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":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Pneumonia; Medicine; Computer science; 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":[0.0008019431,0.0001274568,0.0005971426,0.0006371643,0.0001693709,0.000006849988,0.00004215109,0.00009798877,0.000005991423],"category_scores_gemma":[0.001985643,0.0001084412,0.0001567,0.0009845704,0.00003059973,0.0000407322,0.00004092745,0.0001626532,3.228735e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001368413,"about_ca_system_score_gemma":0.000194401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001728597,"about_ca_topic_score_gemma":0.00007773541,"domain_scores_codex":[0.9987988,0.0001169774,0.0005011259,0.0002424725,0.0002019255,0.0001386471],"domain_scores_gemma":[0.9989325,0.0001017111,0.0002142893,0.0001729434,0.0004935616,0.00008498358],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003675906,0.00008521077,0.659838,0.0003196217,0.0004474894,6.792545e-7,0.006851735,0.002234233,0.3280933,0.00002757298,0.00003106782,0.001703501],"study_design_scores_gemma":[0.001079711,0.0003043851,0.9339353,0.0004178293,0.001469052,0.000002912888,0.0004470509,0.03805077,0.01715762,0.00002476347,0.006973157,0.0001373955],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9827467,0.0001956539,0.01493305,0.0009547501,0.0000483452,0.0008890163,0.0000215423,0.00002914038,0.0001817897],"genre_scores_gemma":[0.9927585,0.00001441058,0.005010769,0.00006807425,0.00001655058,0.00005562339,0.00002665915,0.00001162223,0.002037756],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3109357,"threshold_uncertainty_score":0.4422106,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02038921620244906,"score_gpt":0.3363499094185438,"score_spread":0.3159606932160947,"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."}}