Real-life comparison of posaconazole versus fluconazole for primary antifungal prophylaxis during remission-induction chemotherapy for acute leukemia
Bibliographic record
Abstract
Background: Patients undergoing remission-induction intensive chemotherapy for acute leukemia are at high risk for life-threatening invasive fungal infections (IFIs). Primary antifungal prophylaxis with posaconazole has been shown to reduce the incidence of IFI compared to fluconazole, but real-life data are limited and the effect on mortality remains unclear. Methods: This retrospective cohort study compared fluconazole and posaconazole as primary prophylaxis in real-life practice over a 10-year period, in a Canadian hospital. Results: A total of 299 episodes were included (fluconazole, n = 98; posaconazole, n = 201), of which 68% were first inductions. The underlying hematologic malignancy was acute myeloid leukemia or myelodysplastic syndrome in 88% of episodes and acute lymphoblastic leukemia in 9%. Overall, 20 cases of IFI occurred (aspergillosis, n = 17; candidiasis, n = 3) and 14 were considered as breakthrough IFI. IFI incidence was significantly lower in the posaconazole group (3.5% versus 13.2%; p = 0.001). Empirical or targeted antifungal therapy was also reduced in the posaconazole cohort. Mortality was similar in both groups. Conclusions: In a real-life setting in Canada, primary posaconazole prophylaxis reduces the incidence of IFI during remission-induction chemotherapy, compared to fluconazole.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".