2281 HOW MANY SEMEN SAMPLES ARE REQUIRED TO MAKE THE DIAGNOSIS OF AZOOSPERMIA?
Bibliographic record
Abstract
You have accessJournal of UrologyInfertility: Evaluation1 Apr 20122281 HOW MANY SEMEN SAMPLES ARE REQUIRED TO MAKE THE DIAGNOSIS OF AZOOSPERMIA? Aziz M. Khambati, Keith Jarvi, and Kirk Lo Aziz M. KhambatiAziz M. Khambati Toronto, Canada More articles by this author , Keith JarviKeith Jarvi Toronto, Canada More articles by this author , and Kirk LoKirk Lo Toronto, Canada More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2012.02.2460AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Many guidelines now suggest that men are diagnosed as being azoospermic when no sperm is found in two sequential well collected and analyzed semen samples. It is well known that many men with non-obstructive azoospermia (NOA: the most common cause of azoospermia) will have sperm within the testicles. In addition, it has also been well recognized that all men have a significant degree of variability of sperm counts possibly due to variability in sperm production. This has led us to speculate that some men with NOA may also have variability in sperm production and hence on occasion might have enough sperm production to lead to sperm in the ejaculate. The study objective was to determine how many men who would typically be defined as being azoospermic (azoospermia on two sequential semen analyses) had sperm in the ejaculate on subsequent semen testing. METHODS A retrospective study was performed by using a database containing the semen analyses results of patients referred to our centre between October 2000 and June 2011. Patients with a minimum of three semen samples, each within a space of 6 months, with the first two showing azoospermia were identified. All semen analyses were performed in the same laboratory, with centrifugation of the semen samples and an extensive microscopic analysis of the pellet. Medical records were then reviewed and patients with known obstructive causes such as a vasectomy or congenital bilateral absence of vas deference were excluded. In addition, patients who had undergone a corrective procedure such as a varicocelectomy were also excluded. RESULTS In all, 120 men with a total of 420 semen analyses between them were included in the analysis. In men with two initial azoospermic samples, 27 out of 120 (22.5%) had sperm on the third sample. Eight (29.6 %) of these patients had rare non-motile sperm, whereas the mean and median spermatozoa counts in the remaining men (19/27: 70.4%) was 0.54 and 0.4 million respectively. Four of 41 (9.7%) men with three initial azoospermic samples had spermatozoa on the fourth sample. The average motility overall was 21.4%. Finally, none of the 17 men who were azoospermic after four samples had any sperm identified in their subsequent tests. CONCLUSIONS This study suggests that at least 3 and preferably 4 semen samples should be examined before making the diagnosis of azoospermia. In addition, more than 20% of men who would have originally been diagnosed as azoospermic had enough sperm in the subsequent semen specimens to use in a program of intra-cytoplasmic sperm injection. This might possibly reduce the need for surgery to retrieve sperm surgically for these men. © 2012 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 187Issue 4SApril 2012Page: e920 Advertisement Copyright & Permissions© 2012 by American Urological Association Education and Research, Inc.MetricsAuthor Information Aziz M. Khambati Toronto, Canada More articles by this author Keith Jarvi Toronto, Canada More articles by this author Kirk Lo Toronto, Canada More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.096 | 0.032 |
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".