Levofloxacin prophylaxis in hospitalized children with leukemia: A cost‐utility analysis
Bibliographic record
Abstract
BACKGROUND: Infections are common and are a major cause of morbidity and mortality during treatment of childhood leukemia. We evaluated the cost effectiveness of levofloxacin antibiotic prophylaxis, compared to no prophylaxis, in children receiving chemotherapy for acute myeloid leukemia (AML) or relapsed acute lymphoblastic leukemia (ALL). PROCEDURES: A cost-utility analysis was conducted from the perspective of the single-payer health care system using a lifetime horizon. A comprehensive literature review identified available evidence for effectiveness, safety, costs of antibiotic prophylaxis in children with leukemia, and health utilities associated with the relevant health states. The effects of levofloxacin prophylaxis on health outcomes, quality-adjusted life-years (QALY), and direct health costs were derived from a combined decision tree and state-transition model. One-way deterministic and probabilistic sensitivity analyses were performed to test the sensitivity of results to parameter uncertainty. RESULTS: The literature review revealed one randomized controlled trial on levofloxacin prophylaxis in childhood AML and relapsed ALL, by Alexander et al, that showed a significant reduction in rates of fever and neutropenia (71.2% vs 82.1%) and bacteremia (21.9% vs 43.4%) with levofloxacin compared to no prophylaxis. In our cost-utility analysis, levofloxacin prophylaxis was dominant over no prophylaxis, resulting in cost savings of $542.44 and increased survival of 0.13 QALY. In probabilistic sensitivity analysis, levofloxacin prophylaxis was dominant in 98.8% of iterations. CONCLUSIONS: The present analysis suggests that levofloxacin prophylaxis, compared to no prophylaxis, is cost saving in children receiving intensive chemotherapy for AML or relapsed ALL.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".