Fungal infection and colonization in lung transplant recipients with chronic lung allograft dysfunction
Bibliographic record
Abstract
BACKGROUND: The incidence and impact of de novo fungal airway colonization and infection in lung transplant recipients (LTRs) with known chronic lung allograft dysfunction (CLAD) has not been established. We aimed to determine the 1-year cumulative incidence and risk factors of de novo fungal colonization or infection in LTRs with CLAD and assess the impact of colonization or infection on post-CLAD survival. METHODS: Prospectively collected Toronto Lung Transplant Program database and chart review were used for double-LTRs who were diagnosed with CLAD from January 1, 2016 to January 1, 2020 and who were free of airway fungi within 1 year prior to CLAD onset. International Society for Heart and Lung Transplantation definitions were used to define clinical syndromes. Cox-Proportional Hazards Models were used for risk-factor analysis. Survival analysis could not be completed secondary to low number of fungal events; therefore, descriptive statistics were employed for survival outcomes. RESULTS: We found 186 LTRs diagnosed with CLAD meeting our inclusion criteria. The 1-year cumulative incidence for any fungal event was 11.8% (7.0% for infection and 4.8% for colonization). Aspergillus fumigatus was a causative pathogen in eight of 13 (61.5%) patients with infection and six of nine (66.7%) patients with colonization. No patients with fungal colonization post-CLAD developed fungal infection. Peri-CLAD diagnosis (3 months prior or 1 month after) methylprednisolone bolus (hazards ratio: 8.84, p = .001) increased the risk of fungal events. Most patients diagnosed with fungal infections (53.8%) died within 1-year of CLAD onset. CONCLUSION: De novo IFIs and fungal colonization following CLAD onset were not common. Fungal colonization did not lead to fungal infection. Methylprednisolone bolus was a significant risk factors for post-CLAD fungal events.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".