Cytology‐based screening for anal intraepithelial neoplasia in women with a history of cervical intraepithelial neoplasia or cancer
Bibliographic record
Abstract
BACKGROUND: High-risk human papillomavirus (HPV) has been identified in the pathogenesis of anal cancer. The purpose of this study was to assess the prevalence of abnormal anal cytology and HPV in women aged ≥40 years who have a history of high-grade cervical squamous intraepithelial lesion (SIL) or cancer and to estimate the prevalence of anal intraepithelial neoplasia (AIN) using cytology as the primary screening modality. METHODS: Women who had a history of high-grade cervical SIL or cancer and were ≥40 years of age were included in this prospective study. Anal cytology with HPV-DNA testing was performed. All patients with abnormal anal cytology were referred for high-resolution anoscopy (HRA), and abnormal lesions were biopsied and treated if pathologically confirmed. Abnormal anal cytology correlated with HPV status, HRA findings, and clinical and demographic characteristics. RESULTS: A total of 317 women completed the study. Of these, 96 (30.3%) had abnormal anal cytology (high-grade SIL, 12.5%; low-grade SIL, 19.8%; atypical squamous cells, cannot exclude high-grade SIL, 6.3%; atypical squamous cells of undetermined significance, 61.5%) and 101 (31.9%) were HPV-DNA-positive. There was a significant association between abnormal cytology results and the presence of high-risk HPV. Of the 96 patients with abnormal cytology, 30 (31.3%) had biopsy-proven AIN on HRA, representing 9.5% of the total patient cohort; of these, 10 (33.3%) had low-grade AIN and 20 (66.7%) had high-grade AIN. Older age and smoking were significant risk factors for abnormal anal cytology. CONCLUSION: Women aged ≥40 years with a history of high-grade cervical SIL or cancer have a high rate of AIN. Screening for anal cancer may therefore be considered in this patient population. The optimal screening approach should be addressed in future studies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".