Factors predicting successful sperm retrieval in men with nonobstructive Azoospermia: A single center perspective
Bibliographic record
Abstract
Abstract Predicting successful sperm retrieval is essential in counseling infertile men with Azoospermia. Objectives To assess the predictors of successful sperm extraction in men with nonobstructive Azoospermia. Patients and Methods A retrospective study included all patients with nonobstructive Azoospermia from January 2018 to May 2019. Subdivided into two groups, group I (negative sperm retrieval) and group II (positive sperm retrieval). Results A total of 108 patients with a mean age of 36.8 ± 10 years were included. The rate of successful sperm retrieval was 47.2%. Group I included 57 patients (52.8%) with a mean age of 33.98 ± 6.18, and group II included 51 patients (47.2%) with a mean age of 40.04 ± 12.22 (p = 0.008). Follicular stimulating hormone (FSH) levels were significantly higher in group I (18.55 ± 13 vs. 7.97 ± 7.11; p < 0.004). Similarly, in group I, luteinizing hormone was significantly higher (11.4 ± 7.45 vs. 5.9 ± 4.4; p < 0.001). Age and FSH were the independent predictors of successful micro‐TESE. Additionally, successful pregnancies were 13.7% of patients, 28.6% of which gave rise to living birth. Conclusion Patients' age and serum FSH are independent predictors of successful sperm retrieval for infertile men with nonobstructive Azoospermia; young patients with high FSH levels could have little chance of sperm retrieval.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".