Pharmacoeconomic assessment of therapy for invasive aspergillosis
Bibliographic record
Abstract
Invasive aspergillosis (IA) is a major cause of morbidity and mortality in immunocompromised hosts. Economic expenditures prompted by this invasive fungal infection (IFI) are significant. Although, the duration and associated costs of hospitalization comprise the largest proportion of costs in large surveillance studies, the newer oral antifungal agents may impact significantly on these costs. A review of the pharmacoeconomic (PE) studies is provided focussing on primary therapy, salvage therapy, empiric therapy and prophylaxis for IA. PE evaluations have demonstrated the cost effectiveness and dominance of voriconazole for targeted primary treatment of IA compared with other available agents. Differences in the drug choice and analytic methodology of the PE analyses of empiric antifungal strategy hamper definitive conclusions about the agents employed as empiric antifungal that may be directed at suspected IA although both caspofungin and voriconazole appear to be cost effective and dominant over liposomal amphotericin B (LAmB), whereas LAmB is more costly than conventional amphotericin B. Posaconazole is the most cost-effective agent for antifungal prophylaxis against IFI and IA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".