The effect of ethnicity on semen analysis and hormones in the infertile patient
Bibliographic record
Abstract
INTRODUCTION: We aimed to study the association of ethnicity on semen parameters and hormones in patients presenting with infertility. METHODS: Data from men presenting for infertility assessment were prospectively collected and retrospectively reviewed. Demographic and clinical history was self-reported. Semen analysis included volume, count, motility, morphology, and vitality. The 2010 World Health Organization cutoffs were used. Baseline total testosterone and follicle-stimulating hormone (FSH) levels were recorded. Ethnicity data was classified as Caucasian, African Canadian, Asian, Indo-Canadian, Native Canadian, Hispanic, and Middle Eastern. All patients with complete data were included and statistical analysis was performed. RESULTS: A total of 9079 patients were reviewed, of which 3956 patients had complete data. Of these, 839 (21.2%) were azoospermic. After adjusting for age, African Canadians (odds ratio [OR] 1.70; 95% confidence interval [CI] 1.28-2.25) and Asians (1.34; 95% CI 1.11-1.62) were more likely to be azoospermic compared to Caucasians. Similarly, African Canadians (OR 1.75; 95% CI 1.33-2.29) were more likely to be oligospermic and Asians (OR 0.82; 95% CI 0.70-0.97) less likely to be oligospermic. Low volume was found in African Canadian (OR 1.42; 95% CI 1.05-1.91), Asians (OR 1.23; 95% CI 1.01-1.51), and Indo-Canadians (OR 1.47; 95% CI 1.01-2.13). Furthermore, Asians (OR 0.73; 95% CI 0.57-0.93) and Hispanics (OR 0.58; 95% CI 034-0.99) were less likely to have asthenospermia. Asians (OR 0.73; 95% CI 0.57-0.94) and Indo-Canadians (OR 0.58; 95% CI 0.35-0.99) were less likely to have teratozospermia. No differences were seen for vitality. No differences were seen for FSH levels, however, Asians (p<0.01) and Indo-Canadians (p<0.01) were more likely to have lower testosterone. CONCLUSIONS: Our study illustrates that variations in semen analyses and hormones exist in men with infertility. This may provide insight into the workup and management for infertile men from different ethnicities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".