Guideline for the Management of Fever and Neutropenia in Children With Cancer and Hematopoietic Stem-Cell Transplantation Recipients: 2017 Update
Bibliographic record
Abstract
Purpose To update a clinical practice guideline (CPG) for the empirical management of fever and neutropenia (FN) in children with cancer and hematopoietic stem-cell transplantation recipients. Methods The International Pediatric Fever and Neutropenia Guideline Panel is a multidisciplinary and multinational group of experts in pediatric oncology and infectious diseases that includes a patient advocate. For questions of risk stratification and evaluation, we updated systematic reviews of observational studies. For questions of therapy, we conducted a systematic review of randomized trials of any intervention applied for the empirical management of pediatric FN. The Grading of Recommendation Assessment, Development and Evaluation approach was used to make strong or weak recommendations and to classify levels of evidence as high, moderate, low, or very low. Results Recommendations related to initial presentation, ongoing management, and empirical antifungal therapy of pediatric FN were reviewed; the most substantial changes were related to empirical antifungal therapy. Key differences from our 2012 FN CPG included the listing of a fourth-generation cephalosporin for empirical therapy in high-risk FN, refinement of risk stratification to define patients with high-risk invasive fungal disease (IFD), changes in recommended biomarkers and radiologic investigations for the evaluation of IFD in prolonged FN, and a weak recommendation to withhold empirical antifungal therapy in IFD low-risk patients with prolonged FN. Conclusion Changes to the updated FN CPG recommendations will likely influence the care of pediatric patients with cancer and those undergoing hematopoietic stem-cell transplantation. Future work should focus on closing research gaps and on identifying ways to facilitate implementation and adaptation.
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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 | 0.005 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 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".