{"id":"W4406033899","doi":"10.1093/burnst/tkae073","title":"Aiming for precision: personalized medicine through sepsis subtyping","year":2024,"lang":"en","type":"review","venue":"Burns & Trauma","topic":"Sepsis Diagnosis and Treatment","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Infection and Immunity","funders":"","keywords":"Subtyping; Medicine; Personalized medicine; Sepsis; Context (archaeology); Intensive care medicine; Disease; Presentation (obstetrics); Precision medicine; Bioinformatics; Immunology; Internal medicine; Pathology; Surgery; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002290606,0.0006630736,0.001895251,0.002306934,0.0002915082,0.002146759,0.0007944052,0.001380448,0.003612073],"category_scores_gemma":[0.004257879,0.0002251594,0.001286704,0.001734092,0.0007238142,0.001922261,0.001080722,0.002905549,0.001631369],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008446456,"about_ca_system_score_gemma":0.002487942,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001088738,"about_ca_topic_score_gemma":0.001505951,"domain_scores_codex":[0.9989316,0.0004283143,0.0001354403,0.0001466192,0.0002978251,0.00006029923],"domain_scores_gemma":[0.9973852,0.001871311,0.0001827567,0.00009844873,0.0003941771,0.0000681902],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001056513,0.00008217767,0.00050805,0.03381888,0.000486859,0.0002165616,0.0002098795,0.0006161348,0.001486731,0.01656217,0.02590704,0.9199998],"study_design_scores_gemma":[0.00003886466,0.0001369647,0.001367344,0.02305269,0.0006575466,0.001143321,0.000209324,0.0003389993,0.0008320792,0.01573755,0.9564379,0.00004733653],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0002771506,0.9929457,0.001528955,0.002736505,0.0004622977,0.00001824462,0.00008408843,0.00002919884,0.001917861],"genre_scores_gemma":[0.003317623,0.9916173,0.002252154,0.001613799,0.0005074816,0.00002705339,0.0001356608,0.000007882661,0.0005210203],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003612073,"threshold_uncertainty_score":0.01211405,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3552252667462134,"score_gpt":0.481653693523705,"score_spread":0.1264284267774916,"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."}}