Deep cutaneous fungal infections in solid-organ transplant recipients
Bibliographic record
Abstract
BACKGROUND: Deep cutaneous fungal infections (DCFIs) are varied in immunosuppressed patients, with few data for such infections in solid-organ transplant recipients (s-OTRs). OBJECTIVE: To determine DCFI diagnostic characteristics and outcome with treatments in s-OTRs. METHODS: A 20-year retrospective observational study in France was conducted in 8 primary dermatology-dedicated centers for s-OTRs diagnosed with DCFIs. Relevant clinical data on transplants, fungal species, treatments, and outcomes were analyzed. RESULTS: Overall, 46 s-OTRs developed DCFIs (median delay, 13 months after transplant) with predominant phaeohyphomycoses (46%). Distribution of nodular lesions on limbs and granulomatous findings on histopathology were helpful diagnostic clues. Treatments received were systemic antifungal therapies (48%), systemic antifungal therapies combined with surgery (28%), surgery alone (15%), and modulation of immunosuppression (61%), leading to complete response in 63% of s-OTRs. LIMITATIONS: Due to the retrospective observational design of the study. CONCLUSIONS: Phaeohyphomycoses are the most common DCFIs in s-OTRs. Multidisciplinary teams are helpful for optimal diagnosis and management.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".