{"id":"W4362470802","doi":"10.2196/42452","title":"Real-Time Prediction of Sepsis in Critical Trauma Patients: Machine Learning–Based Modeling Study","year":2023,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Sepsis Diagnosis and Treatment","field":"Medicine","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Science and Technology Major Project; Government of Jiangsu Province; National Natural Science Foundation of China","keywords":"Sepsis; Medicine; Vital signs; Intensive care unit; Emergency medicine; Machine learning; Early warning score; Systemic inflammatory response syndrome; Intensive care medicine; Internal medicine; Surgery; Computer science","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":[0.003062508,0.0006512927,0.0005229967,0.0008803316,0.0002284026,0.0006220594,0.0006605642,0.0005430678,0.001270656],"category_scores_gemma":[0.00601966,0.0001839623,0.001052476,0.0007005509,0.0001988835,0.0005759498,0.0004972585,0.001024768,0.0003141641],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005664434,"about_ca_system_score_gemma":0.0007487558,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004086241,"about_ca_topic_score_gemma":0.002147861,"domain_scores_codex":[0.9993528,0.0003041769,0.0000483804,0.0001306809,0.00009138586,0.00007259899],"domain_scores_gemma":[0.9969207,0.001925834,0.0003762602,0.0002271697,0.0003492169,0.0002009589],"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":[0.0009597218,0.001523874,0.8287878,0.0001127511,0.0007661828,0.0004134868,0.0001944106,0.1294973,0.0007859626,0.0005761214,0.002004895,0.0343775],"study_design_scores_gemma":[0.00003964654,0.0005897883,0.1339871,0.00004413562,0.0001448051,0.0002556095,0.0001664492,0.8629643,0.0004829873,0.0006812749,0.0006190487,0.00002488989],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9902707,0.000318705,0.008034253,0.0004091266,0.00003103015,0.00005618663,0.0004169461,0.00004706614,0.0004160512],"genre_scores_gemma":[0.9961482,0.0001812674,0.002568918,0.00005285651,0.00002698148,0.00004002936,0.0007834631,0.000008134305,0.0001901778],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004086241,"threshold_uncertainty_score":0.01619625,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2102623458967224,"score_gpt":0.4874708212569214,"score_spread":0.277208475360199,"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."}}