{"id":"W4404041705","doi":"10.1097/01.pcc.0001085004.05282.39","title":"PP124 Topic: AS13–Hematology/Oncology/Stem Cell Transplant/Immunology: ARE EXISTING MORTALITY PREDICTION MODELS SUITABLE FOR PEDIATRIC HEMATOLOGY/ONCOLOGY PATIENTS ADMITTED TO THE PEDIATRIC INTENSIVE CARE UNIT DUE TO SEPSIS?","year":2024,"lang":"en","type":"article","venue":"Pediatric Critical Care Medicine","topic":"Hematological disorders and diagnostics","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children","funders":"","keywords":"Medicine; Hematology; Sepsis; Internal medicine; Pediatric oncology; Intensive care medicine; Oncology; Intensive care unit; Cancer","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","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0007409728,0.0008516917,0.002251113,0.0009052567,0.0005283526,0.00005275568,0.0005912106,0.001333526,0.0001424392],"category_scores_gemma":[0.006956773,0.0006078315,0.0004127746,0.002144903,0.0003106478,0.0001676371,0.0003074948,0.001549701,0.0001566965],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005010171,"about_ca_system_score_gemma":0.0008456084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001129593,"about_ca_topic_score_gemma":0.0001256115,"domain_scores_codex":[0.9935594,0.000471704,0.001838459,0.001611082,0.0008541604,0.001665165],"domain_scores_gemma":[0.9870754,0.006954884,0.0002306312,0.0008329707,0.003804399,0.001101646],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.001091481,0.0008738743,0.9007674,0.01397191,0.0001880677,0.002716588,0.02423132,0.0001408333,0.00001070586,0.006504202,0.04633834,0.003165265],"study_design_scores_gemma":[0.03538355,0.06581391,0.3247332,0.002498319,0.05385523,0.004479864,0.4240615,0.006344608,0.0004637438,0.0084609,0.06744233,0.006462832],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9046673,0.0521454,0.01182782,0.01772855,0.004891909,0.004575523,0.00120729,0.0006582071,0.002298016],"genre_scores_gemma":[0.9875762,0.0013404,0.0006452961,0.005440856,0.002894855,0.001169642,0.0006946559,0.0001175341,0.0001205149],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5760342,"threshold_uncertainty_score":0.9999629,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07061640900605488,"score_gpt":0.3637459236905054,"score_spread":0.2931295146844505,"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."}}