Evaluation of Cervical Cancer Screening in Japan: Challenges and Future Directions for Negative Intraepithelial Lesion or Malignancy/High-Risk Human Papillomavirus Positive Case Management
Bibliographic record
Abstract
Cervical cancer screening is crucial for early detection and prevention. In Japan, women with negative intraepithelial lesion or malignancy (NILM) and high-risk human papillomavirus (HR-HPV) positivity are recommended retest for 12 months, rather than immediate colposcopy. International guidelines differ, and often prioritize early colposcopy for persistent HPV16/18 infections. This study evaluates Japan's current screening approach, and identifies areas for improvement. A retrospective cohort study analyzed cervical cancer screening data from Saga Prefecture (2019-2021), assessing follow-up adherence, colposcopy referral rates, and CIN2+ and CIN3+ detection among NILM/HR-HPV+ cases. Among 27,789 individuals screened, 2248 (8.1%) were NILM/HR-HPV+. Follow-up adherence after 12 months was 54.4%. Of these, 132 with cytological abnormalities underwent colposcopy, revealing CIN2+ in 27.3% of cases. Additionally, 561 women with persistent NILM/HR-HPV+ underwent colposcopy, with CIN2+ in 7.6% and CIN3+ in 3.9% of cases. Japan's current NILM/HR-HPV+ management strategy could delay the detection of high-grade cervical lesions. International guidelines favor earlier colposcopy referrals, particularly for HPV16/18+ cases. To improve cervical cancer prevention, Japan should consider a risk-based stratification model, enhance follow-up adherence, expand colposcopy access, and develop a national patient tracking system. Adopting primary HPV-based screening could attain the best global practices, facilitating earlier detection and reducing cervical cancer.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".