Clinical parameters as predictors for sperm retrieval success in azoospermia: experience from Indonesia
Bibliographic record
Abstract
Background: Azoospermia is the most severe type of male infertility. This study aimed to identify useful clinical parameters to predict sperm retrieval success. This could assist clinicians in accurately diagnosing and treating patients based on the individual clinical parameters of patients. Methods: A retrospective cohort study was performed involving 517 patients with azoospermia who underwent sperm retrieval in Jakarta, Indonesia, between January 2010 and April 2023. Clinical evaluation and scrotal ultrasound, serum follicle stimulating hormone (FSH), luteinizing hormone (LH), and testosterone levels were evaluated before surgery. Multivariate analyses were conducted to determine clinical parameters that could predict overall sperm retrieval success. Further subgroup analysis was performed to determine the factors that the diagnosis of non-obstructive azoospermia (NOA) diagnosis and sperm retrieval success among patients with NOA. Results: A total of 2,987 infertile men attended our clinic. Men with azoospermia (n=517) who met the inclusion criteria and did not fulfil any exclusion criteria were included in the study. The overall sperm retrieval success was 47.58%. Logistic regression revealed that FSH 7.76 mIU/mL (sensitivity: 60.1%, specificity: 63.3%, p<0.001); longest testicular axis length 3.89 cm (sensitivity: 33.6%, specificity: 41.6%); and varicocele (p<0.001) were independent factors for overall sperm retrieval. The FSH cutoff of 7.45 mIU/mL (sensitivity: 31.3%, specificity: 37.7%, p<0,001); longest testicular axis length 3.85 cm (sensitivity: 76.7%, specificity: 65.4%, p<0.001); and varicocele (p<0.001) were independent factors for NOA diagnosis. Varicocele was the only clinical parameter that significantly predicted the success of sperm retrieval in patients with NOA. Conclusions: FSH, LH, longest testicular axis, and varicocele are among the clinical parameters that are useful for predicting overall sperm retrieval success and NOA diagnosis. However, varicocele is the only clinical parameter that significantly predicts sperm retrieval success in patients with NOA. High-quality studies are required to assess the other predictors of sperm retrieval success.
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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 | 0.000 | 0.001 |
| 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".