Dry self-sampling versus provider-sampling of cervicovaginal specimens for human papillomavirus detection in the Inuit population of Nunavik, Quebec
Bibliographic record
Abstract
OBJECTIVE: To assess the comparability of self-collected cervicovaginal samples and provider-collected cervical samples for the detection of human papillomavirus (HPV) DNA among Inuit women in Nunavik, Quebec, avoiding the use of liquid-based storage and transport of the self-collected samples. METHODS: Ninety-three women aged 18-69 years were recruited from a previously formed cohort on the natural history of HPV to this cross-sectional measurement study. This study utilized HPV DNA test results from 89 paired specimens collected by study participant and health provider with Dacron swabs. Samples were tested for 36 HPV types with the PGMY-primer PCR protocol and genotyping with the linear array method. Unweighted kappa statistics and McNemar tests were used to measure the agreement between sampling techniques. RESULTS: In the self-collected samples, 30 different HPV types were found, compared with 29 types found in the provider-collected samples. The prevalence of high-risk (HR) HPV was 38.2% in the self-collected samples and 28.1% in the provider-collected samples. The agreement between collection methods for the detection of HR-HPV DNA (85.4%) was good. HR-HPV and type-specific HPV 16/18 were as likely to be detected in the self-collected samples compared with the provider-obtained samples. CONCLUSIONS: Women in this population were easily able to collect adequate cervicovaginal specimens for HPV testing. As self-sampling has a high recovery of HR-HPV and is comparable with provider-sampling, we conclude that self-sampling with dry storage and transport could be a good cervical cancer screening alternative for Inuit women in Nunavik who have traditionally avoided speculum examination.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".