Invasive fungal infections: epidemiology and analysis of antifungal prescriptions in onco-haematology
Bibliographic record
Abstract
WHAT IS KNOWN AND OBJECTIVE: Invasive fungal infections (IFI) are associated with high rates of morbidity and mortality, particularly in onco-haematology patients. We aimed to study the epidemiology of IFI in neutropenic patients and estimate the economic impact of treatment of those infections. METHODS: All patients hospitalized in onco-haematology, and treated with antifungal agents, in 2005 were investigated. Four features were studied: the diagnosis for each patient, the antifungal drugs used, the thoracic densitometry reports and the sero-mycological data. Infectious episodes were stratified according to the EORTC 2008 classification criteria (10). RESULTS AND DISCUSSION: Of the 1130 patients surveyed, 192 patients received systemic antifungal agents. Of these 46% had acute leukaemia, 29% bone-marrow allografts, 7% lymphoma and 18% other malignant haemopathies. Using the EORTC 2008 criteria (10), there were 8 proved IFI (3 aspergillosis, 3 candidosis and 2 other IFI), 17 probable IFI (11 aspergillosis, 6 candidosis) and 16 possible aspergillosis. The incidence of IFI was 2·1%. Eighty patients (41·7%) had received prophylaxis: 56 with fluconazole and 24 with voriconazole. Treatment was most often empirical (n = 127, 66·1%). Combination of two antifungals was used in 17 cases. The mean duration of prophylactic, empirical, proved/probable aspergillosis-directed, candidaemia-directed and combination treatment was 19, 19, 46, 32 and 27 days, respectively. The cost of antifungal treatment in 2005 reached almost 2,000,000 €, including 427,000 € for documented infections (proved and probable), 1,246,000 € for empirical treatment and 58,300 € for prophylaxis. WHAT IS NEW AND CONCLUSION: The incidence of IFI is low but the pharmacoeconomic impact is extremely high. Improved strategies are required to reduce the frequency and duration of empirical treatment without compromising beneficial outcome.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".