Successful long‐term management of invasive cerebral fungal infection following liver transplantation
Bibliographic record
Abstract
Central nervous system (CNS) infections after liver transplantation may be fungal in aetiology, with involvement from either common organisms such as Cryptococcus neoformans and Aspergillus spp. as well as less common organisms, such as the Mucorales and Scedosporium spp. Although the mortality of CNS fungal infections was nearly 100% in early series, more recent data has suggested that good outcomes can be achieved. This may be due to both improved diagnostic capabilities, such as the ability to obtain fungal susceptibilities and therapeutic drug levels, and improved therapeutic options, such as the newer triazoles- voriconazole and posaconazole. Due to improved outcomes, issues have now arisen around the long-term tolerability of these agents. The following two cases of invasive cerebral fungal infections following liver transplantation, one with Aspergillus flavus, and the other with Scedosporium boydii/apiospermum highlight the success that can be seen with the modern management of a previously fatal diagnosis. In particular, we highlight the issues around therapeutic monitoring and discontinuation of therapy.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".