{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004084484,0.0008404563,0.003553328,0.0003030926,0.0001499359,0.00005577863,0.0002200291,0.0004742325,0.001121712],"category_scores_gemma":[0.0002485807,0.0005097734,0.001847291,0.000523484,0.0001661957,0.0000700969,0.00006406385,0.0004524592,0.0002794162],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004640765,"about_ca_system_score_gemma":0.0002527985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006184474,"about_ca_topic_score_gemma":0.000008457053,"domain_scores_codex":[0.9967155,0.00008173602,0.001073382,0.001033935,0.0005493626,0.0005461072],"domain_scores_gemma":[0.9977498,0.0009087923,0.0002941605,0.0006905081,0.0001343118,0.0002224332],"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":[0.00003687712,0.0001998534,0.00000576293,0.0268632,0.001793483,0.0001534661,0.0007101876,6.620996e-8,2.162912e-7,0.0009150495,0.05901389,0.9103079],"study_design_scores_gemma":[0.001688196,0.0005243343,0.000008925285,0.09630115,0.02058959,0.0003080863,0.0001525208,0.000006725725,0.000003605742,0.000439766,0.8796082,0.0003688957],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00001476059,0.9896388,0.0002711144,0.002164297,0.001074287,0.003140148,0.0001237209,0.0001998221,0.003373023],"genre_scores_gemma":[0.00004885723,0.9883014,0.001782646,0.000518387,0.001848709,0.001641843,0.0007290901,0.0002190593,0.004910005],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9099391,"threshold_uncertainty_score":0.9997914,"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."}}