Incidence and outcomes of invasive fungal infection among solid organ transplant recipients: A population‐based cohort study
Bibliographic record
Abstract
BACKGROUND: Invasive fungal infection (IFI) in solid organ transplant (SOT) recipients is associated with significant morbidity and mortality. The long-term probability of post-transplant IFI is poorly understood. METHODS: We conducted a population-based cohort study using linked administrative healthcare databases from Ontario, Canada, to determine the incidence rate; 1-, 5-, and 10-year cumulative probabilities of IFI; and post-IFI all-cause mortality in SOT recipients from 2002 to 2016. We also determined post-IFI, death-censored renal allograft failure. RESULTS: We included 9326 SOT recipients (median follow-up: 5.35 years). Overall, the incidence of IFI was 8.3 per 1000 person-years. The 1-year cumulative probability of IFI was 7.4% for lung, 5.4% for heart, 1.8% for liver, 1.2% for kidney-pancreas, and 1.1% for kidney-only allograft recipients. Lung transplant recipients had the highest incidence rate and 10-year probability of IFI: 43.0 per 1000 person-years and 26.4%, respectively. The 1-year all-cause mortality rate after IFI was 34.3%. IFI significantly increased the risk of mortality in SOT recipients over the entire follow-up period (hazard ratio: 6.50, 95% CI: 5.69-7.42). The 1-year probability of death-censored renal allograft failure after IFI was 9.8%. CONCLUSION: Long-term cumulative probability of IFI varies widely among SOT recipients. Lung transplantation was associated with the highest incidence of IFI with considerable 1-year all-cause mortality.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".