Value-Based Care for Healthy Children With First Episode of Febrile Neutropenia
Bibliographic record
Abstract
OBJECTIVES: There is a lack of guidance on the management of febrile neutropenia in otherwise healthy children, including the need for hospitalization and antibiotic administration, leading to significant practice variation in management. The aim of this initiative was to decrease the number of unnecessary hospitalizations and empirical antibiotics prescribed by 50% over a 24-month period for well-appearing, previously healthy patients older than 6 months presenting to the emergency department with a first episode of febrile neutropenia. METHODS: A multidisciplinary team of stakeholders was assembled to develop a multipronged intervention strategy using the Model for Improvement. A guideline for the management of healthy children with febrile neutropenia was created, coupled with education, targeted audit and feedback, and reminders. Statistical control process methods were used to analyze the primary outcome of the percentage of low-risk patients receiving empirical antibiotics and/or hospitalization. Balancing measures included missed serious bacterial infection, emergency department (ED) return visit, and a new hematologic diagnosis. RESULTS: Over the 44-month study period, the mean percentage of low-risk patients hospitalized and/or who received antibiotics decreased from 73.3% to 12.9%. Importantly, there were no missed serious bacterial infections, no new hematologic diagnoses after ED discharge, and only 2 ED return visits within 72 hours without adverse outcomes. CONCLUSIONS: A guideline for the standardized management of febrile neutropenia in low-risk patients increases value-based care through reduced hospitalizations and antibiotics. Education, targeted audit and feedback, and reminders supported sustainability of these improvements.
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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".