Anticipating the Impact of Human Papillomavirus Vaccination on US Cervical Cancer Prevention Strategies
Bibliographic record
Abstract
Cervical cancer prevention guidelines are benchmarked to risk of cervical precancer. In younger age cohorts, vaccination against high-risk types of human papillomavirus (HPV) has reduced HPV 16/18 prevalence and cervical intraepithelial neoplasia. Lower prevalence of precancer will impair the sensitivity of cytology and colposcopy, but negative predictive value will rise. Training and skills maintenance will become more difficult as abnormalities become less common. Primary screening with HPV assays will become more attractive but will require HPV genotyping as most positive HPV tests will reflect non-16/18 infections with lower oncogenicity. Screening will begin later and will occur at longer intervals. Colposcopy and treatment thresholds will become more stringent. Historical data sets will become inappropriate for guidelines development. As women immunized using nonavalent vaccine reach screening age, these trends will become still more pronounced.
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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 | 0.009 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".