Economic evaluation of azoles as primary prophylaxis for the prevention of invasive fungal infections in Spanish patients undergoing allogeneic haematopoietic stem cell transplant
Bibliographic record
Abstract
Patients undergoing allogeneic haematopoietic stem cell transplantation (alloHSCT) are at risk of developing invasive fungal infections (IFIs). Even with introduction of oral triazole antifungal agents (fluconazole, itraconazole, posaconazole and voriconazole) IFI-associated morbidity and mortality rates and economic burden remain high. Despite their proven efficacy, it is currently unknown which is the most cost-effective antifungal prophylaxis (AFP) agent. To determine the costs and outcomes associated with AFP, a decision-analytic model was used to simulate treatment in a hypothetical cohort of 1000 patients undergoing alloHSCT from the perspective of the Spanish National Health System. Generic itraconazole was the least costly AFP (€162) relative to fluconazole (€500), posaconazole oral suspension (€8628) or voriconazole (€6850). Compared with posaconazole, voriconazole was associated with the lowest number of breakthrough IFIs (36 vs 60); thus, the model predicted fewer deaths from breakthrough IFI for voriconazole (24) than posaconazole (33), and the lowest predicted costs associated with other licensed antifungal treatment and IFI treatment in a cohort of 1000. Voriconazole resulted in cost savings of €4707 per patient compared with posaconazole. Itraconazole demonstrated a high probability of being cost-effective. As primary AFP in alloHSCT patients 180 days posttransplant, voriconazole was more likely to be cost-effective than posaconazole regarding cost per additional IFI and additional death avoided.
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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 | 0.004 | 0.014 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".