Cost effectiveness of outpatient treatment for febrile neutropaenia in adult cancer patients
Bibliographic record
Abstract
BACKGROUND: There is uncertainty whether low-risk episodes of febrile neutropaenia (FN) in adult cancer patients are best managed in the in- or outpatient setting. METHODS: A Monte Carlo cost-utility model was created to compare four treatment strategies for low-risk FN: (1) treatment in hospital with intravenous antibiotics (HospIV); (2) early discharge after 48 h in-patient observation, followed by oral outpatient treatment (EarlyDC); (3) outpatient management with IV antibiotics (HomeIV); and (4) outpatient management with oral antibiotics (HomePO). The model used a health-care payer perspective and a time horizon of one FN episode. Outcome measures were quality-adjusted FN episodes (QAFNE), costs (Canadian dollars) and incremental cost-effectiveness ratios (ICER). Parameter uncertainty was assessed with probabilistic sensitivity analyses. RESULTS: HomePO was cost saving ($3470 vs $4183), but less effective (0.65 QAFNE vs 0.72 QAFNE) than HomeIV. The corresponding ICER was $10,186 per QAFNE. Both EarlyDC ($6115; 0.66 QAFNE) and HospIV ($13,557; 0.62 QAFNE) were dominated strategies. At a willingness-to-pay (WTP) threshold of $4,000 per QAFNE, HomePO and HomeIV were cost effective in 54 and 38% of simulations, respectively. INTERPRETATION: For adult cancer patients with an episode of low-risk FN, treatment in hospital is more expensive and less effective than outpatient strategies.
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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 | 0.015 |
| 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 | 0.001 |
| 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".