The relationship between chronic diseases and number of sexual partners: an exploratory analysis
Bibliographic record
Abstract
BACKGROUND: We investigated sex-specific associations between lifetime number of sexual partners and several health outcomes in a large sample of older adults in England. METHODS: We used cross-sectional data from 2537 men and 3185 women aged ≥50 years participating in the English Longitudinal Study of Ageing. Participants reported the number of sexual partners they had had in their lifetime. Outcomes were self-rated health and self-reported limiting long-standing illness, cancer, coronary heart disease, and stroke. We used logistic regression to analyse associations between lifetime number of sexual partners and health outcomes, adjusted for relevant sociodemographic and health-related covariates. RESULTS: Having had 10 or more lifetime sexual partners was associated with higher odds of reporting a diagnosis of cancer than having had 0-1 sexual partners in men (OR 1.69, 95% CI 1.01 to 2.83) and women (OR 1.91, 95% CI 1.04 to 3.51), respectively. Women who had 10 or more lifetime sexual partners also had higher odds of reporting a limiting long-standing illness (OR 1.64, 95% CI 1.15 to 2.35). No other statistically significant associations were observed. CONCLUSIONS: A higher lifetime number of sexual partners is associated with increased odds of reported cancer. Longitudinal research is required to establish causality. Understanding the predictive value of lifetime number of sexual partners as a behavioural risk factor may improve clinical assessment of cancer risk in older adults.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".