Low-dose prednisone is an effective rescue for deteriorating semen parameters following vasovasostomy
Bibliographic record
Abstract
OBJECTIVE: This retrospective study aimed to evaluate the effectiveness of low-dose prednisone as a rescue therapy for patients with deteriorating semen parameters following vasovasostomy. MATERIALS AND METHODS: Electronic medical records were queried at the University of Miami with documented CPT code 55400 (Bilateral Vasovasostomy) between January 2016 and April 2023. Records were then reviewed to identify patients who demonstrated ≥50% decrease in semen parameters, specifically sperm concentration, motility and total motile sperm count. Patients who were treated with 6 weeks of low-dose prednisone were identified, and baseline semen parameters and subsequent changes after prednisone therapy were assessed. A Mann-Whitney U Test was used to compare semen parameter changes before and after prednisone. Adverse effects associated with prednisone were monitored. RESULTS: A total of 8 patients were identified with deteriorating semen parameters who were treated with 6 weeks of low-dose prednisone. Following prednisone therapy, all patients demonstrated improvements in total motile sperm count (TMSC), with a median improvement of 6 million. The median relative improvement in TMSC was 433%. Sperm concentration and motility also improved compared to post-operative baseline. No adverse effects were reported during the treatment period. CONCLUSIONS: Low-dose prednisone therapy appears to be a safe and effective intervention for managing deteriorating semen parameters following VV. The observed improvements in TMSC suggest the potential of prednisone to rescue patients with delayed failure after VV. Further research with larger sample sizes is warranted to confirm the safety and efficacy of low-dose prednisone as a rescue therapy in this specific patient population. Optimizing VV outcomes is crucial in male infertility, and further exploration of steroid therapy and innovative biotechnologies is warranted.
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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) | 0.000 | 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".