Medical management of non-obstructive azoospermia: A systematic review
Why this work is in the frame
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Bibliographic record
Abstract
While most men with non-obstructive azoospermia (NOA) are not amenable to medical treatment, some men can be treated effectively with hormonal therapy, prior to considering surgery. In some cases, hormonal therapy alone can treat NOA, without the need for surgery. In other cases, correction of a potential hormonal imbalance can enhance the chances of success of surgical sperm retrieval (SSR), with either conventional or microdissection testicular sperm extraction. Abnormal testicular function and low androgen levels can result from a primary dysfunction, a medical or surgical condition, or from an exogenous factor, and should be managed prior to more invasive interventions. Even men with normal androgen levels may benefit from hormonal therapy before sperm retrieval. Moreover, SSR may cause testicular injury and aggravate the pre-existing situation. If surgical extraction of sperm fails, it leaves the patients with less satisfactory options, like donor sperm or adoption. Therefore, it is the role of the infertility specialist to be vigilant and identify reversible causes of NOA, such as hormonal imbalance, prior to considering surgery. In the present paper we will systematically review the literature and highlight the available conventional medical regimens, as well as experimental ones.Abbreviations: ART: assisted reproductive technology; CAH: congenital adrenal hyperplasia; EAU: European Association of Urology; hCG: human chorionic gonadotrophin; HH: hypogonadotrophic hypogonadism; hMG: human menopausal gonadotrophin; IUI: intrauterine insemination; micro-TESE: microdissection testicular sperm extraction; NOA: non-obstructive azoospermia; OR: odds ratio; SCO: Sertoli-cell only; SERM: selective oestrogen receptor modulator; SRR: sperm retrieval rate; SSC: spermatogonia stem cell; TART: testicular adrenal rest tumour; WMD: weighted mean difference
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it