Sirolimus May Reduce Fertility in Male Renal Transplant Recipients
Bibliographic record
Abstract
Assessment of sex hormones in organ transplant recipients suggests that sirolimus may impair testicular function. The aim of this study was to evaluate the frequency and severity of sirolimus-associated alterations in sperm parameters and their impact on fathered pregnancy rate. An observational study was carried out in male patients aged 20-40 years who received a kidney transplant during 1995-2005. Patients were sent a questionnaire by post, and sperm analysis was proposed. The fathered pregnancy rates according to the immunosuppressive regimen were estimated and compared using the Poisson model. Complete information was obtained from 95 out of 116 recipients. Patients treated with sirolimus throughout the post-transplant period had a significantly reduced total sperm count compared to patients who did not receive sirolimus (28.6 +/- 31.2 x 10(6) and 292.2 +/- 271.2 x 10(6), respectively; p = 0.006), and a decreased proportion of motile spermatozoa (22.2 +/- 12.3% and 41.0 +/- 14.5%, p = 0.01). Moreover, the fathered pregnancy rate (pregnancies/1000 patient years) was 5.9 (95% CI, 0.8-42.1) and 92.9 (95% CI, 66.4-130.0) in patients receiving sirolimus-based and sirolimus-free regimens, respectively (p = 0.007). Of six patients in whom sirolimus treatment was interrupted, only three showed a significant improvement in sperm parameters. Sirolimus is associated with impaired spermatogenesis and, as a corollary, may reduce male fertility.
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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 | 0.002 |
| 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".