Vaginal self sampling versus physician cervical sampling for HPV among younger and older women
Bibliographic record
Abstract
OBJECTIVES: To estimate the agreement between self collected vaginal swabs and physician collected cervical brush samples for detection of oncogenic human papillomavirus infection (HPV) by the hybrid capture 2 (HC-2) test among women younger and older than 50 years, and to assess women's preference for sample collection method based on age. METHODS: Consecutive women aged 15-49 years due for a 1 year visit in a prevalence study of carcinogenic HPV and a new sample of women aged 50 years and older attending their family physicians for cervical screening, in Ontario, Canada, performed vaginal self sampling and underwent physician cervical sampling and cervical cytology. Women completed a self administered questionnaire on demographics and preference for sampling method. RESULTS: Among the 307 women aged 15-49 years, the prevalence of HPV was 20.8% (64/307) and 17.6% (54/307) in the vaginal and cervical specimens, respectively. Among the women aged 50 years and older, prevalence was 9.9% (15/152) and 8.6% (13/152), respectively. Kappa for agreement between sample collection methods was 0.54 for the younger and 0.37 for the older women (both p< 0.001). Nearly half of the women preferred self sampling or had no preference. CONCLUSIONS: There was fair agreement between self collected vaginal and physician collected cervical specimens for detecting carcinogenic HPV in younger and older women. Vaginal sampling for HPV appears to be promising as a primary screening strategy for cervical cancer prevention programmes in low resource settings in developed and developing countries.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".