Does testicular sperm retrieval adversely impact spermatogenesis over the long‐term?
Bibliographic record
Abstract
Testicular sperm retrieval (TSR) techniques are valuable in the context of severe idiopathic male factor infertility; however, there are few studies in the literature examining the long-term impact of TSR on testicular function. The objective was to determine whether testicular sperm aspiration (TESA) or microdissection testicular sperm extraction (micro-TESE) worsens the pre-existing spermatogenesis deficiency in men with either cryptozoospermia or severe oligozoospermia. The study population consisted of 145 men with either cryptozoospermia or severe oligozoospermia that underwent TESA or micro-TESE and had long-term post-operative semen analyses (SA). Patients with SA prior to and following TSR were included (n = 24). Amongst them, 16 men underwent TESA and 8 underwent micro-TESE. The follow-up SA was obtained at a mean of 3.0 ± 2.0 years following TSR (range: 0.3-8.3 years) amongst all participants. The post-operative semen parameters in the TESA group were similar to the pre-intervention parameters (p > 0.1). Similarly, the micro-TESE cohort did not demonstrate significant alterations in semen parameters post-intervention (p > 0.05). None of the men in the study became azoospermic following the TSR. Our study indicates TESA or micro-TESE do not appear to worsen the pre-existing spermatogenesis deficiencies in cryptozoospermic and oligozoospermic men over a long-term period. Larger studies are required to corroborate these findings.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".