Preoperative follicle-stimulating hormone: A factor associated with semen parameter improvement after microscopic subinguinal varicocelectomy
Bibliographic record
Abstract
INTRODUCTION: Currently, there exists no serum biomarker to predict patients likely to benefit from varicocelectomy. The purpose of this study was to assess the association between baseline follicle-stimulating hormone (FSH) and semen parameter changes after subinguinal microscopic varicocelectomy. METHODS: We retrospectively reviewed all men who underwent microscopic subinguinal varicocelectomy between August 2015 and October 2018. Pre- and postoperative semen analyses were stratified per total motile sperm count (TMSC): TMSC <5, 5-9, and >9 million (based on TMSC required for in vitro fertilization, intrauterine insemination [IUI], and natural conception, respectively). Then, variables were analyzed to determine the correlation with postoperative TMSC values and upgrade in TMSC category. RESULTS: Among the 66 men analyzed, 55 (83.3%) and 11 (16.7%) had a preoperative TMSC of <5 million and 5-9 million, respectively. A total of 33 (50%) patients upgraded in TMSC category, 26 of them achieving levels corresponding to natural conception and seven achieving those of IUI. Additionally, a significant correlation was observed between postoperative TMSC and preoperative TMSC (r=0.528; p<0.001), and preoperative FSH (r=-0.314; p=0.010). A lower preoperative FSH (odds ratio [OR] 0.82; 95% confidence interval [CI] 0.68-0.98; p=0.028) and a higher preoperative TMSC (OR 1.37; 95% CI 1.06-1.76; p=0.015) were associated with upgrade in TMSC category. CONCLUSIONS: Lower preoperative FSH and higher TMSC are associated with improvement in TMSC category after varicocelectomy, although small sample size limited the study. FSH can be useful to identify men who are most likely to benefit from varicocele repair.
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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".