Potential Association Between Male Infertility and Occupational Psychological Stress
Bibliographic record
Abstract
Learning Objectives Recall past findings associating sperm parameters with occupational exposure to chemicals or work-related psychological distress Understand in what occupational, health, and demographic respects workers with male factor infertility differ from those seen for female infertility. Note which if any aspects of occupational stress were associated with male infertility in this study. Identify any clinical implications of these findings. The purpose of this work was to investigate the influence of working conditions, occupational exposures to potential reproductive toxic agents, and psychological stress on male fertility. The study population consisted of 202 consecutive male patients attending a fertility clinic. Of those, 106 patients had attended the clinic because of a male infertility problem (case group), 66 patients had attended the clinic because of a female infertility problem (control group), and 30 patients had a combined infertility problem (male and female). Male infertility was associated with working in industry and construction as compared with other occupations (78.6% vs 58.3%, P = 0.044). Industry and construction workers were of lower educational level than the other workers (mean: 12.1 vs 13.4 years, P = 0.021). These patients also tended to smoke more than the other workers (OR = 2.53, 95% CI = 1.08 to 5.98), more often worked in shifts (OR = 3.12, 95% CI = 1.19 to 8.13), reported physical exertion in work (OR = 3.35, 95% CI = 1.44 to 7.80), and were more exposed to noise and welding (OR = 3.84, 95% CI = 1.63 to 9.14, OR = 4.40, 95% CI = 1.11 to 1.76, respectively). Male infertility (case group) was found to be statistically related to higher marks in all four measures of burnout as compared with the controls. The largest difference was obtained in the measure of cognitive weariness (mean:2.9vs 2.1, P < 0.001). In a multiple logistic regression analysis, industry and construction jobs (adjusted OR = 2.2, 95% CI 1.2 to 2.7) and cognitive weariness (adjusted OR = 1.8, 95% CI = 1.03 to 4.6) were found to be independent risk factors for male infertility problems. Male infertility was independently associated with industry and construction jobs as well as job burnout.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.005 | 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".