HPV Self-Sampling for Cervical Cancer Screening in Under-Screened Saskatchewan Populations: A Pilot Study
Bibliographic record
Abstract
Of all cancers in female Canadians, the most rapidly increasing incidence is that of cervical cancer. The objective of this pilot study was to assess how HPV self-sampling might improve cervical cancer screening participation in both urban and rural settings in Saskatchewan, one of the most sparsely populated provinces in Canada. Study groups consisted of n = 250 participants to whom self-swabbing kits were mailed with instructions and n = 250 participants to whom kits were handed out in 6 urban and rural clinics. The inclusion criteria selected subjects aged 30–69 years who were Saskatchewan residents for at least 5 years with valid health coverage, had a cervix, and had no record of cervical cancer screening in 4 years. The returned samples were analyzed for specific HPV strains using the Roche Molecular Diagnostics Cobas 4800® System. The overall response rate was ~16%, with the response to the handout distribution being roughly double that of the mailout. While HPV positivity did not differ across the distribution groups, participants at a specific inner-city clinic reported significantly higher positivity to at least one HPV strain as compared to any other clinic and all mailouts combined. For this high-risk population, in-person handout of self-sampling kits may be the most effective means of improving screening.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".