Cost-effectiveness of Outpatient Management for Febrile Neutropenia in Children With Cancer
Bibliographic record
Abstract
OBJECTIVE: Inpatient management remains the standard of care for treatment of febrile neutropenia (FN) in children with cancer. Clinical data suggest, however, that outpatient management might be a safe and efficacious alternative for patients with low-risk FN episodes. METHODS: A cost-utility model was created to compare 4 treatment strategies for low-risk FN. The base case considered pediatric cancer patients with low-risk FN. The model used a health care payer's perspective and a time horizon of 1 FN episode. Four treatment strategies were evaluated: (1) entire treatment in hospital with intravenous antibiotics (HospIV); (2) early discharge consisting of 48 hours of inpatient observation with intravenous antibiotics followed by oral outpatient treatment (EarlyDC); (3) entirely outpatient management with intravenous antibiotics (HomeIV); and (4) entirely outpatient management with oral antibiotics (HomePO). Outcome measures were quality-adjusted FN episodes (QAFNEs), costs (Canadian dollars), and incremental cost-effectiveness ratios. Parameter uncertainty was assessed with probabilistic sensitivity analyses. RESULTS: The most cost-effective strategy was HomeIV. It was cost-saving ($2732 vs $2757) and more effective (0.66 vs 0.55 QAFNE) as compared with HomePO. EarlyDC was slightly more effective (0.68 QAFNE) but significantly more expensive ($5579) than HomeIV, which resulted in an unacceptably high incremental cost-effectiveness ratio of more than $130 000 per QAFNE. HospIV was the least cost-effective strategy because it was more expensive ($14 493) and less effective (0.65 QAFNE) than EarlyDC. CONCLUSION: The findings of this decision-analytic model indicate that the substantially higher costs of inpatient management cannot be justified on the basis of safety and efficacy considerations or patient/parent preferences.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".