Voriconazole and squamous cell carcinoma after lung transplantation: A multicenter study
Bibliographic record
Abstract
This study evaluated the independent contribution of voriconazole to the development of squamous cell carcinoma (SCC) in lung transplant recipients, by attempting to account for important confounding factors, particularly immunosuppression. This international, multicenter, retrospective, cohort study included adult patients who underwent lung transplantation during 2005-2008. Cox regression analysis was used to assess the effects of voriconazole and other azoles, analyzed as time-dependent variables, on the risk of developing biopsy-confirmed SCC. Nine hundred lung transplant recipients were included. Median follow-up time from transplantation to end of follow-up was 3.51 years. In a Cox regression model, exposure to voriconazole alone (adjusted hazard ratio 2.39, 95% confidence interval 1.31-4.37) and exposure to voriconazole and other azole(s) (adjusted hazard ratio 3.45, 95% confidence interval 1.07-11.06) were associated with SCC compared with those unexposed after controlling for important confounders including immunosuppressants. Exposure to voriconazole was associated with increased risk of SCC of the skin in lung transplant recipients. Residual confounding could not be ruled out because of the use of proxy variables to control for some confounders. Benefits of voriconazole use when prescribed to lung transplant recipients should be carefully weighed versus the potential risk of SCC. EU PAS registration number: EUPAS5269.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".