A Case for Offering HPV Self-Sampling to Well-Screened Women. Comment on Lesack et al. Willingness to Self-Collect a Sample for HPV-Based Cervical Cancer Screening in a Well-Screened Cohort: HPV FOCAL Survey Results. Curr. Oncol. 2022, 29, 3860–3869
Bibliographic record
Abstract
Lesack et al. recently published a cross-sectional study that focused on human papillomavirus (HPV) self-sampling in the screened population, a population not conventionally thought of for HPV self-sampling. They found 52% of well-screened, highly educated women who participated in the Human Papillomavirus For Cervical Cancer (HPV FOCAL) screening trial in British Columbia, Canada, would be willing to self-collect an HPV sample. We published a similar study in 2021 on well-screened, highly educated women affiliated with a family medicine clinic in Edmonton, Alberta, Canada, and found that 60% of these women preferred to have the option of HPV self-sampling. Our findings reinforce Lesack et al.'s results and together provide evidence for offering HPV self-sampling as an option for the well-screened population.
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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 | 0.010 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.074 | 0.065 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".