An Immune Reconstitution Syndrome-Like Illness Associated withCryptococcus neoformans Infection in Organ Transplant Recipients
Bibliographic record
Abstract
BACKGROUND: We describe an immune reconstitution syndrome (IRS)-like entity in the course of evolution of Cryptococcus neoformans infection in organ transplant recipients. METHODS: The study population comprised a cohort of 83 consecutive organ transplant recipients with cryptococcosis who were observed for a median of 2 years in an international, multicenter study. RESULTS: In 4 (4.8%) of the 83 patients, an IRS-like entity was observed a median of 5.5 weeks after the initiation of appropriate antifungal therapy. Worsening of clinical manifestations was documented, despite cultures being negative for C. neoformans. These patients were significantly more likely to have received tacrolimus, mycophenolate mofetil, and prednisone as the regimen of immunosuppressive therapy than were all other patients (P = .007). The proposed basis of this phenomenon is reversal of a predominantly Th2 response at the onset of infection to a Th1 proinflammatory response as a result of receipt of effective antifungal therapy and a reduction in or cessation of immunosuppressive therapy. CONCLUSIONS: This study demonstrated that an IRS-like entity occurs in organ transplant recipients with C. neoformans infection. Furthermore, this entity may be misconstrued as a failure of therapy. Immunomodulatory agents may have a role as adjunctive therapy in such cases.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".