{"id":"W4411436820","doi":"10.1093/bioadv/vbaf140","title":"Prediction of the infecting organism in peritoneal dialysis patients with acute peritonitis using interpretable Tsetlin Machines","year":2024,"lang":"en","type":"article","venue":"Bioinformatics Advances","topic":"Dialysis and Renal Disease Management","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Infection and Immunity","funders":"Engineering and Physical Sciences Research Council; Medical Research Council; National Institute for Health and Care Research","keywords":"Peritoneal dialysis; Peritonitis; Organism; Medicine; Dialysis; Intensive care medicine; Computational biology; Internal medicine; Biology; Genetics","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":[],"consensus_categories":[],"category_scores_codex":[0.0001071565,0.000118753,0.00022458,0.0001724235,0.00006251015,0.00005914523,0.00007081398,0.00002897904,0.00002959586],"category_scores_gemma":[0.00003390784,0.00006496841,0.00009297156,0.0005126626,0.00006222141,0.0003861176,0.00007817748,0.00009879476,0.000002650026],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006975119,"about_ca_system_score_gemma":0.00005895582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003940665,"about_ca_topic_score_gemma":0.00002466698,"domain_scores_codex":[0.9990743,0.00001762238,0.00037566,0.0001015975,0.0002950465,0.0001358071],"domain_scores_gemma":[0.9996068,0.00002516806,0.0001073972,0.0001586699,0.00006331701,0.00003857778],"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":[0.0002823832,0.0006143738,0.8028719,0.004628418,0.001554452,0.00003462587,0.01057871,0.005616982,0.001473043,0.0004144374,0.000092811,0.1718379],"study_design_scores_gemma":[0.002030099,0.0005060603,0.2527952,0.004104689,0.002248261,0.00002436906,0.001718903,0.7295621,0.001977138,0.0001427782,0.004534848,0.0003555914],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9952987,0.0003901204,0.001780818,0.00005338311,0.0003307628,0.0003449058,0.00009143097,0.00003492625,0.001674995],"genre_scores_gemma":[0.9962252,0.0001353834,0.003442002,0.00004148086,0.00004506184,0.000009618922,0.00004546033,0.00001278078,0.0000430107],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7239451,"threshold_uncertainty_score":0.2649335,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007110007031997439,"score_gpt":0.2391215279493388,"score_spread":0.2320115209173414,"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."}}