Invasive aspergillosis in liver transplant recipients
Bibliographic record
Abstract
BACKGROUND: Liver transplantation is increasing worldwide with underlying pathologies dominated by metabolic and alcoholic diseases in developed countries. METHODS: We provide a narrative review of invasive aspergillosis (IA) in liver transplant (LT) recipients. We searched PubMed and Google Scholar for references without language and time restrictions. RESULTS: The incidence of IA in LT recipients is low (1.8%), while mortality is high (∼50%). It occurs mainly early (<3 months) after LT. Some risk factors have been identified before (corticosteroid, renal, and liver failure), during (massive transfusion and duration of surgical procedure), and after transplantation (intensive care unit stay, re-transplantation, re-operation). Diagnosis can be difficult and therefore requires full radiological and clinicobiological collaboration. Accurate identification of Aspergillus species is recommended due to the cryptic species, and susceptibility testing is crucial given the increasing resistance of Aspergillus fumigatus to azoles. It is recommended to reduce the dose of tacrolimus (50%) and to closely monitor the trough level when introducing voriconazole, isavuconazole, and posaconazole. Surgery should be discussed on a case-by-case basis. Antifungal prophylaxis is recommended in high-risk patients. Environmental preventative measures should be implemented to prevent outbreaks of nosocomial aspergillosis in LT recipient units. CONCLUSION: IA remains a very serious disease in LT patients and should be promptly sought and, if possible, prevented by clinicians when risk factors are identified.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".