Guideline for the Management of Fever and Neutropenia in Children With Cancer and/or Undergoing Hematopoietic Stem-Cell Transplantation
Bibliographic record
Abstract
PURPOSE: To develop an evidence-based guideline for the empiric management of pediatric fever and neutropenia (FN). METHODS: The International Pediatric Fever and Neutropenia Guideline Panel is a multidisciplinary and multinational group composed of experts in pediatric oncology and infectious disease as well as a patient advocate. The Panel was convened for the purpose of creating this guideline. We followed previously validated procedures for creating evidence-based guidelines. Working groups focused on initial presentation, ongoing management, and empiric antifungal therapy. Each working group developed key clinical questions, conducted systematic reviews of the published literature, and compiled evidence summaries. The Grades of Recommendation Assessment, Development, and Evaluation approach was used to generate summaries, and evidence was classified as high, moderate, low, or very low based on methodologic considerations. RESULTS: Recommendations were made related to initial presentation (risk stratification, initial evaluation, and treatment), ongoing management (modification and cessation of empiric antibiotics), and empiric antifungal treatment (risk stratification, evaluation, and treatment) of pediatric FN. For each recommendation, the strength of the recommendation and level of evidence are presented. CONCLUSION: This guideline represents an evidence-based approach to FN specific to children with cancer. Although some recommendations are similar to adult-based guidelines, there are key distinctions in multiple areas. Implementation will require adaptation to the local context.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".