Is oral azithromycin effective for the treatment of cyclosporine-induced gingival hyperplasia in cardiac transplant recipients?
Bibliographic record
Abstract
Anecdotal evidence suggests that azithromycin is effective for the treatment of cyclosporine-induced gingival hyperplasia in solid-organ transplant recipients. We present the cases of two heart transplant patients who insidiously developed gingival hyperplasia, likely because of immunosuppression with cyclosporine, which was treated with azithromycin. Evidence supporting the efficacy of azithromycin in the treatment of cyclosporine-induced gingival hyperplasia in solid organ transplant recipients was searched for, identified, and then critically assessed. While no data were found specifically evaluating azithromycin in cardiac transplant patients, there were nine pertinent papers identified that evaluated the clinical question of interest in the renal transplant population [Wahlstrom et al. (1995) The New England Journal of Medicine 332, 753; Boran et al. (1996) Transplantation Proceedings 28, 2316; Gomez et al. (1997) Nephrology Dialysis Transplantation 12, 2694; Ljutic (1997) Dialysis & Transplantation 26, 787; Puig et al. (1997) Transplantation Proceedings 29, 2379; Nash et al. (1998) Transplantation 65, 1611; Nowicki et al. (1998) Annals of Transplantation 3, 25; Wirnsberger et al. (1998) Transplantation Proceedings 30, 2117; Citterio et al. (2001) Transplantation Proceedings 33, 2134]. These studies and case reports are summarized. While more evidence is required to support routine use of azithromycin for the treatment of cyclosporine-induced gingival hyperplasia in cardiac transplant recipients, preliminary published evidence from renal transplant patients is certainly favourable.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".