Global Andrology Forum (GAF) Clinical Guidelines on the Management of Non-obstructive Azoospermia: Bridging the Gap between Controversy and Consensus
Bibliographic record
Abstract
PURPOSE: Non-obstructive azoospermia (NOA), defined as the absence of sperm in the ejaculate due to testicular failure, is observed in 5% to 15% of infertile men and accounts for two-thirds of azoospermia cases. The management of NOA is marked by significant controversy and global variation in diagnostic and therapeutic approaches, highlighting the crucial need for well-designed and standardized clinical practice guidelines. We present comprehensive graded clinical practice recommendations and statements for diagnosing and treating NOA, aiming to establish standardized strategies that can globally help guide practitioners in their practice. MATERIALS AND METHODS: A comprehensive literature review was conducted to gather evidence on the epidemiological, diagnostic, and therapeutic aspects of NOA. The Global Andrology Forum (GAF) recommendations were developed through the collaboration of a global panel of experts using the Delphi method and surveys to achieve consensus. Statements were graded according to the Oxford Centre for Evidence-Based Medicine "GRADE" classification as either "Strong" or "Weak." Statements receiving at least 80% expert consensus were graded as "Strong," while others were categorized as "Weak." RESULTS: The GAF has formulated a total of 49 recommendations and statements on the diagnosis and treatment of NOA, including 21 for diagnosis and 28 for treatment. The recommendations and statements were evaluated and graded by a panel of 48 GAF experts from 25 countries worldwide. The majority of experts (60.5%) had more than 10 years of clinical experience in managing NOA. CONCLUSIONS: The GAF guidelines address discrepancies in NOA management across diverse clinical settings and provide comprehensive graded recommendations to guide clinicians in its diagnosis and treatment. Developed and graded by a large worldwide panel of experts, the current guidelines present simplified, high-standard strategies that can be seamlessly integrated into the daily global practice, offering practitioners a clear framework for managing NOA.
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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 | 0.087 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".