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?
Bibliographic record
Abstract
Aims & Objectives: Children with hemato-oncological diseases or post-stem cell transplantation (SCT), are highly susceptible to life-threatening infections, constituting a significant portion of pediatric intensive care unit (PICU) admissions due to sepsis. Standard pediatric prognostic tools may not adequately assess illness severity and mortality risk in this subgroup. Our study aimed to assess the suitability of the Pediatric Logistic Organ Dysfunction-2 (PELOD-2) and the Pediatric Risk of Mortality III (PRISM III) scores in this context and develop a tailored risk-assessment model for these vulnerable patients. Methods: We conducted a retrospective cohort study, at the largest referral pediatric hematology-oncology center in Israel. We collected and analyzed demographics, clinical and laboratory data, and PICU outcomes from all admissions to the PICU due to sepsis of children with hemato-oncological diseases or after SCT, between 2008-2021 (n=233). Results: The survival rate was 83%. The diagnostic capabilities of PELOD-2 and PRISM III, as determined by the area under the receiver operating characteristic curve (AUC), were 82% and 74%, respectively. Models including the existing scoring tools and 9 new clinical parameters (age, SCT, viral or fungal infection, central venous line removal, vasoactive inotropic score, bilirubin, C-reactive protein level, and prolonged neutropenia) significantly improved the above AUCs to 90% (p=0.01) and 87% (p<0.001), respectively. Conclusions: PELOD-2 and PRISM III scores show limited diagnostic accuracy in hemato-oncological children admitted to the PICU with sepsis. Our findings underscore the necessity for a specialized risk-assessment tool, reflective of their distinct features, to be validated in a large multi-center prospective study. Keywords: oncology, Sepsis, PELOD-2
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".