Assessment of anti-sperm antibodies in couples after testicular sperm extraction
Bibliographic record
Abstract
PURPOSE: Testicular spermatozoa can be retrieved successfully by the testicular sperm extraction (TESE) procedure and used for intracytoplasmic sperm injection. Disruption in the blood-testis barrier can lead to the production of antisperm antibodies (ASA). The aim of this prospective study was to investigate the frequency of ASA formation in couples after TESE procedure. METHODS: Thirty-seven couples were included in the study at the Urology Clinic of the Dr. Zekai Tahir Burak Women's Health Training and Research Hospital. History, physical examination, spermiogram, and endocrine profiles were obtained for all male patients. All the male patients in this study had been diagnosed with nonobstructive azoospermia (NOA) and underwent microdissection TESE. Secondary and tertiary cases were also included in the study. Serum samples were obtained from all 74 patients before TESE, and at three and 12 months after TESE. Serum ASA levels were determined. ANOVA was performed for statistical analysis for serum Follicle-Stimulating Hormone (FSH), testosterone and testicular volume. P < 0.05 was considered significant. RESULTS: There were no differences in the testicular volumes, serum FSH and testosterone levels before and after TESE. None of the patients or their partners developed significant levels of ASA as a result of the TESE procedure. CONCLUSION: TESE procedure does not cause ASA production in either males or their female partners.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".