Prevention and Management of Infectious Complications in Pediatric Patients With Cancer: A Survey Assessment of Current Practices Across Children's Oncology Group Institutions
Bibliographic record
Abstract
INTRODUCTION: While clinical practice guidelines (CPGs) for pediatric oncology infection prophylaxis and management exist, few data describe actual management occurring at pediatric oncology centers. METHODS: An electronic survey querying infection management practices in nontransplant pediatric oncology patients was iteratively created by the Children's Oncology Group (COG) Cancer Control and Supportive Care Infectious Diseases Subcommittee and sent to leaders at all COG institutions, limiting each site to one response to represent their institution. RESULTS: The response rate was 57% (129/227 institutions). Many sites reported utilizing COG-endorsed CPGs for antibacterial (76%) and antifungal prophylaxis (74%), and fever and neutropenia (FN, 64%). Most institutions reported using antimicrobial prophylaxis for patients with acute myeloid leukemia (88% antibacterial, 100% antifungal) and relapsed acute lymphoblastic leukemia (82% antibacterial, 95% antifungal). Definitions of fever, phagocyte recovery, and antibiotic duration in febrile patients varied. Most institutions administer empiric broad-spectrum antibiotics for nonneutropenic fever, although 14% reported withholding antibiotics based on initial clinical status or risk stratification tools. Most respondents reported (70%) admitting FN patients for at least 48 h, however 15% have low-risk FN protocols allowing outpatient management. FN patients remain admitted on antibiotics through count recovery in 50% of institutions, whereas the others employed various early discharge/early antibiotic discontinuation strategies. CONCLUSIONS: There is often consistency but also substantial variability in reported antimicrobial prophylaxis strategies and management of patients with fever and represents an opportunity for implementation studies to standardize application of CPG recommendations and randomized trials to advance evidence where knowledge gaps exist.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".