Occupation exposures and sperm morphology: a case-referent analysis of a multi-centre study
Bibliographic record
Abstract
OBJECTIVE: We examined occupational exposures and sperm morphology to establish whether exposures implicated differed from those affecting motile sperm concentration. METHODS: Computer aided sperm morphometric assessment was undertaken on morphology slides obtained as part of a multi-centre study in 1999-2002 of occupational factors in male infertility. Men attending 14 fertility clinics across the UK were recruited and gave a semen sample. Before results of the semen analysis were known, the men completed detailed questionnaires about their employment and lifestyle. Occupational exposures were assessed by occupational hygienists. Data were analysed using an unmatched case-referent design, allowing for clustering and for confounders. Three case definitions were used: poor morphology (normal morphology <4%), low motile sperm count (MSC) (<4.8×10(6)) and either condition. RESULTS: Morphology results were available for 1861/2011 men employed at the time of recruitment. Of these 1861, 296 (15.9%) had poor morphology; of the 2011with sperm count, 453 (22.5%) had low MSC; 654/1981 (33.0%) had either condition. Poor morphology, adjusted for confounding, was related to self-reported lifetime exposure to lead (OR=1.33; 95% CI 1.00 to 1.75). Low MSC was also related to self-reported lead and to hygienist-assessed glycol ether exposure. Self-reported use of paint stripper (OR=1.47; 95% CI 1.07 to 2.03) and lead, but not glycol ether, were significantly related to the combined case definition. CONCLUSIONS: While this study did not identify any occupational exposure uniquely related to sperm morphology, the capacity of the study to detect risk was increased by including morphology with sperm concentration and motility.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".