Randomized Intervention of Self-Collected Sampling for Human Papillomavirus Testing in Under-Screened Rural Women: Uptake of Screening and Acceptability
Bibliographic record
Abstract
BACKGROUND: Our aim was to determine if cervical cancer screening uptake would increase among under-screened women living in rural Ontario, Canada, if at-home self-collected sampling for human papillomavirus (HPV) testing was offered as a primary cervical cancer screening modality, compared to invited papanicolaou (Pap) testing or routine opportunistic screening. METHODS: Women 30-70 years of age who were overdue for cervical cancer screening were randomized to receive (1) an at-home self-collected HPV kit, (2) a reminder invitation for Pap testing, or (3) standard of care opportunistic screening. The first two arms were also asked demographic and screening history questions. Women randomized to arm 1 were asked about acceptability. RESULTS: In total, 818 eligible women were identified in a small rural community in Southwestern Ontario: 335 received a self-collected HPV testing kit, 331 received a reminder letter, and 152 received standard of care. In the HPV self-collection arm, 21% (70/335) returned the sample and questionnaire and 11% (37/335) opted to undergo Pap testing. In total, 32% from the HPV self-collection arm, 15% (51/331) from the Pap invitation arm, and 8.5% (13/152) with standard of care were screened. Women receiving the self-collected HPV kit were 3.7 (95% confidence interval 2.2-6.4) times more likely to undergo screening compared to the standard of care arm. In the HPV self-sampling arm, 80% (56/70) said they would be very likely to choose self-collected sampling in the future. CONCLUSIONS: Providing self-collected sampling for HPV testing was more effective than sending reminder letters to increase screening coverage in under-screened women.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".