Association of Serum 25-Hydroxyvitamin D With Prevalence, Incidence, and Clearance of Vaginal HPV Infection in Young Women
Bibliographic record
Abstract
BACKGROUND: We assessed the association between serum 25-hydroxyvitamin D levels and genital human papillomavirus (HPV) prevalence, incidence, and clearance among female participants in the HPV Infection and Transmission among Couples through Heterosexual activity (HITCH) Cohort Study. METHODS: We genotyped HPV DNA in vaginal samples and quantified baseline serum 25-hydroxyvitamin D levels using Roche's Linear Array and Total vitamin D assay, respectively. We used logistic and Cox proportional hazards models, respectively, to estimate adjusted odds ratios (ORs) and hazard ratios (HRs) with 95% confidence intervals (CIs). RESULTS: There was no association between vitamin D levels (every 10-ng/mL increase) at baseline and HPV prevalence (OR, 0.88; 95% CI, .73-1.03) or incidence (HR, 0.88; 95% CI, .73-1.06), but we observed a modest negative association with HPV clearance (HR, 0.76; 95% CI, .60-.96). Vitamin D levels <30 ng/mL, compared with those ≥30 ng/mL, were not associated with HPV prevalence (OR, 0.98; 95% CI, .57-1.69) or incidence (HR, .87; 95% CI, .50-1.43), but they were associated with a marginally significant increased clearance (OR, 2.14; 95% CI, .99-4.64). We observed consistent results with restricted cubic spline modeling of vitamin D levels and clinically defined categories. HPV type-specific analyses accounting for multiple HPV infections per participant showed no association between vitamin D levels and all study outcomes. CONCLUSIONS: This study provided no evidence of an association between low vitamin D levels and increased HPV prevalence, acquisition, or clearance.
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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 | 0.000 | 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) | 0.001 | 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".