Evolution and future of cervical cancer screening: from cytology to primary HPV testing and the impact of vaccination
Bibliographic record
Abstract
INTRODUCTION: Cervical cancer remains a significant global health challenge despite decades of progress in screening and prevention. Global cervical cancer screening practices vary substantially, with many countries still relying on cytology-based methods, despite evidence supporting the superior performance of human papillomavirus (HPV)-based screening. AREAS COVERED: This review explores the historical evolution as well as current landscape and policies of cervical cancer screening, with a focus on Western countries. We discuss the gradual transition from cytology to HPV DNA testing as the primary screening method, while recognizing the continuing role of cytology as a triage method. We also argue that HPV vaccination will have a transformative impact on screening practices, necessitating the need for adapting screening strategies to a post-vaccination world. EXPERT OPINION: The role of cytology in cervical cancer screening will become increasingly limited due to its diminished effectiveness post-HPV vaccination, as many abnormal cytology results will likely be false positives. This could lead to unnecessary procedures, underscoring the need for adjustments in screening strategies and HPV testing to align with the fact that cervical precancerous lesions will become exceedingly rare.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".