Human papillomavirus testing for primary screening of cervical cancer precursors.
Bibliographic record
Abstract
Our objective was to determine whether the addition of human papillomavirus (HPV) testing to screening cytology improves the detection of cervical cancer precursors. Women of ages 18-69 years underwent conventional Pap cytology and HPV DNA testing in a multicenter study in Newfoundland, Canada. Those with positive cytology and/or HPV and a random sample of those with dual negative results were referred for colposcopy. The study enrolled 2098 women. The relative sensitivity of HPV testing was significantly higher than cytology for all-grade squamous intraepithelial lesions [SILs; 73%; 95% confidence interval (CI), 62-82] and high grade SILs (HSILs; 90%; 95% CI, 74-97) but had lower relative specificity (62% for all-grade SILs and 51% for HSILs) than most cytological cutpoints. The rate of combined correct results for all-grade lesions was higher for HPV testing (68.8%) than for any cytological cutpoint (equivocal, 52.3%; LSILs, 51.6%; HSILs, 44.5%). The combination of HPV and an LSIL cutpoint had a negative predictive value of 68% (95% CI, 52-80) for all SILs and 100% (95% CI, 91-100) for HSILs, while referring for colposcopy only 12% of the women. We concluded that HPV testing in conjunction with cytology improved the screening efficacy of cytology alone and may allow for a more effective and safe primary screening program with increased screening intervals.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".