Cervical human papillomavirus detection is not affected by menstrual phase
Bibliographic record
Abstract
OBJECTIVES: In many settings, human papillomavirus (HPV) DNA testing already plays an important role in cervical cancer screening. It is unclear whether hormonal fluctuations associated with menstrual phase or oral contraceptive (OC) use have any effect on HPV detection. We evaluated the effects of OC use and timing of cervical sampling in relation to women's last menstrual period (LMP) on HPV detection, and viral load in the Brazilian Ludwig-McGill cohort study. METHODS: Women in the cohort were followed every 4-6 months, and at each clinic visit they were asked to complete a questionnaire and to provide a cervical sample for HPV testing. Specimens from 6093 patient visits (n=2209 women) were categorised according to date of LMP into four distinct phases: follicular (days 5-9), midcycle (days 10-15), luteal (days 16-22), or late luteal (days 23-31). RESULTS: Compared with follicular phase (referent group), HPV detection did not differ according to reported LMP for midcycle (OR=1.14, 95% CI 0.95 to 1.37), luteal (OR=1.03, 95% CI 0.85 to 1.25), or late luteal menstrual phase (OR=1.01, 95% CI 0.83 to 1.24), and was also not influenced by OC use. Analyses restricted to high-risk HPV types (grouped) and HPVs 16 and 18 (separately), produced similar non-significant associations. For HPV-positive samples, we found that the menstrual phase did not influence the total viral load. CONCLUSIONS: These results indicate HPV detection is not associated with menstrual phase. Our findings suggest that standardising the timing of specimen collection for HPV testing is not necessary.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".