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Record W4411521899 · doi:10.1080/10428194.2025.2521650

Risk factors for mortality and re-admission of children with hematological malignancies to the intensive care unit due to sepsis

2025· article· en· W4411521899 on OpenAlexaff
Shlomit Barzilai‐Birenboim, David M. Zucker, Galia Avrahami, Sarah Elitzur, Salvador Fisher, Sarah Ganem, Gil Gilead, Shai Izraeli, Gili Kadmon, Eytan Kaplan, Aviva C. Krauss, Elhanan Nahum, Ron Rabinowicz, Jerry Stein, Osnat Tausky, Avichai Weissbach, Talya Wittmann Dayagi, Joanne Yacobovich, Asaf Yanir

Bibliographic record

VenueLeukemia & lymphoma/Leukemia and lymphoma · 2025
Typearticle
Languageen
FieldMedicine
TopicNeutropenia and Cancer Infections
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersIsrael Cancer Association
KeywordsSepsisMedicineIntensive care medicineIntensive care unitPediatric intensive care unitEmergency medicineInternal medicine

Abstract

fetched live from OpenAlex

Children with hematological malignancies, are often hospitalized in pediatric intensive care units (PICU) for sepsis with high mortality and re-admission rates. Risk factors for both are inconsistent. We reviewed data of 190 admissions of children with hematological malignancies to PICU for sepsis. Survival rate (SR) was 85%. Mortality risk factors were: non-complete remission (p < 0.01) and status post-stem cell transplantation (p = 0.02), and best predictors were inotropic drugs (p < 0.01), and Pediatric logistic organ dysfunction-2 (p < 0.01) scores. Patients with viremia had the lowest SR (50%, 0.001). One-quarter of the children were re-admitted due to sepsis, and risk factors were: High-risk (HR) hematological malignancy (p < 0.01) and lack of central venous line (CVL) removal (p < 0.01). Sepsis remains a major cause of death in children with hematological malignancies, and re-admissions are common. Our findings support the recommendation of removing CVL during sepsis and highlight those at the highest risk for sepsis to consider individualized anti-infectious prophylaxis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.291
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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