Testicular versus percutaneous epididymal sperm aspiration for patients with obstructive azoospermia: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Intracytoplasmic sperm injection (ICSI) is a popular treatment for male infertility due to obstructive azoospermia (OA). Testicular sperm aspiration (TESA) and percutaneous epididymal sperm aspiration (PESA) are two common sperm retrieval approaches for ICSI among men with OA. However, the comparative efficacies of TESA and PESA have been debated for more than a decade and there has been no synthesis of the available evidence. This meta-analysis compared fertility outcomes between TESA and PESA among men with OA undergoing ICSI. Methods: We searched Embase, PubMed, ScienceDirect, and Web of Science to identify studies comparing the effectiveness of TESA and PESA for ICSI. Study quality was assessed using the Newcastle-Ottawa scale. Data were pooled using a random-effects model. Outcomes were fertilization rate, implantation rate, pregnancy rate, and miscarriage rate. Results are expressed as odds ratio (OR) with 95% confidence intervals (CIs). Study heterogeneity was evaluated by the I-square (I2) statistic. Results: Of 2,965 references retrieved, eight studies met eligibility criteria. These studies included 2,020 men receiving 2,060 ICSI cycles. The pooled results showed no significant differences in pregnancy and miscarriage rates between TESA and PESA groups, but TESA yielded a significantly higher implantation rate than PESA (OR =1.58, P=0.02, I2=24%). Conclusions: TESA and PESA yielded similar pregnancy and miscarriage rates for couples receiving ICSI because of OA, but each demonstrated unique advantages and disadvantages. Further studies are required to evaluate safety outcomes and efficacy for specific clinical groups.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.030 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".