Evidence of Decreased Long-term Risk of Cervical Precancer after Negative Primary HPV Screens Compared with Negative Cytology Screens in a Longitudinal Cohort Study
Bibliographic record
Abstract
BACKGROUND: The growing use of primary human papillomavirus (HPV) cervical cancer screening requires determining appropriate screening intervals to avoid overtreatment of transient disease. This study examined the long-term risk of cervical precancer after HPV screening to inform screening interval recommendations. METHODS: This longitudinal cohort study (British Columbia, Canada, 2008 to 2022) recruited women and individuals with a cervix who received 1 to 2 negative HPV screens (HPV1 cohort, N = 5,546; HPV2 cohort, N = 6,624) during a randomized trial and women and individuals with a cervix with 1 to 2 normal cytology results (BCS1 cohort, N = 782,297; BCS2 cohort, N = 673,778) extracted from the provincial screening registry. All participants were followed through the registry for 14 years. Long-term risk of cervical precancer or worse [cervical intraepithelial neoplasia grade 2 or worse (CIN2+)] was compared between HPV and cytology cohorts. RESULTS: Cumulative risks of CIN2+ were 3.2/1,000 [95% confidence interval (CI), 1.6-4.7] in HPV1 and 2.7/1,000 (95% CI, 1.2-4.2) in HPV2 after 8 years. This was comparable with the risk in the cytology cohorts after 3 years [BCS1: 3.3/1,000 (95% CI, 3.1-3.4); BCS2: 2.5/1,000 (95% CI, 2.4-2.6)]. The cumulative risk of CIN2+ after 10 years was low in the HPV cohorts [HPV1: 4.7/1,000 (95% CI, 2.6-6.7); HPV2: 3.9 (95% CI, 1.1-6.6)]. CONCLUSIONS: Risk of CIN2+ 8 years after a negative screen in the HPV cohorts was comparable with risk after 3 years in the cytology cohorts (the benchmark for acceptable risk). IMPACT: These findings suggest that primary HPV screening intervals could be extended beyond the current 5-year recommendation, potentially reducing barriers to 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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".