Guideline for the Management of Fever and Neutropenia in Pediatric Patients With Cancer and Hematopoietic Cell Transplantation Recipients: 2023 Update
Bibliographic record
Abstract
PURPOSE: To update a clinical practice guideline (CPG) for the empiric management of fever and neutropenia (FN) in pediatric patients with cancer and hematopoietic cell transplantation recipients. METHODS: The International Pediatric Fever and Neutropenia Guideline Panel reconvened to conduct the second update of this CPG. We updated the previous systematic review to identify new randomized controlled trials (RCTs) evaluating any strategy for the management of FN in pediatric patients. Using the Grading of Recommendations Assessment, Development and Evaluation framework, evidence quality was classified as high, moderate, low, or very low. The panel updated recommendations related to initial management, ongoing management, and empiric antifungal therapy. Changes from the 2017 CPG were articulated, and good practice statements were considered. RESULTS: We identified 10 new RCTs in addition to the 69 RCTs identified in previous FN CPGs to inform the 2023 FN CPG. Changes from the 2017 CPG included two conditional recommendations regarding (1) discontinuation of empiric antibacterial therapy in clinically well and afebrile patients with low-risk FN if blood cultures remain negative at 48 hours despite no evidence of marrow recovery and (2) pre-emptive antifungal therapy for invasive fungal disease in high-risk patients not receiving antimold prophylaxis. The panel created a good practice statement to initiate FN CPG-consistent empiric antibacterial therapy as soon as possible in clinically unstable febrile patients. CONCLUSION: The updated FN CPG incorporates important modifications on the basis of recently published trials. Future work should focus on addressing knowledge gaps, improving CPG implementation, and measuring the impact of CPG-consistent care.
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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 | 0.010 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".