Cryptococcosis in HIV-negative Patients with Renal Dialysis: A Retrospective Analysis of Pooled Cases
Bibliographic record
Abstract
Cryptococcosis is a lethal fungal infection mainly caused by Cryptococcus neoformans/C. gattii species. Currently, our understanding of cryptococcosis episodes in HIV-negative patients during renal dialysis remains scarce and fragmented. Here, we performed an analysis of pooled cases to systemically summarize the epidemiology and clinical characteristics of cryptococcosis among HIV-negative patients with renal dialysis. Using pooled data from our hospital and studies identified in four medical databases, 18 cases were identified and analyzed. The median duration time of renal dialysis for peritoneal renal dialysis and hemodialysis cases was 8 months and 36 months, respectively. Several non-neoformans/gattii species were identified among the renal dialysis recipients with cryptococcosis, particularly Cryptococcus laurentii and Cryptococcus albidus, which share similar clinical manifestations as those caused by C. neoformans and C. gattii. Our analyses suggest that physicians should consider the possibility of the occurrence of cryptococcosis among renal dialysis recipients even when cryptococcal antigen test result is negative. The timely removal of the catheter is crucial for peritoneal dialysis patients with cryptococcosis. In addition, there is a need for optimized antifungal treatment strategy in renal dialysis recipients with cryptococcal infections.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".