The Incidence of Vaginal Intraepithelial Neoplasia 2+ in Patients With Previous Hysterectomy Cervival Intraepithelial Neoplasia 3+ Between 2005–2015: A Population-Based Study
Bibliographic record
Abstract
OBJECTIVES: In light of the rarity of vaginal cancer, the role of vaginal vault testing following hysterectomy for preinvasive or early cervical cancers is unclear. The objective was to determine the subsequent risk of VaIN2/3, and invasive vaginal cancer following hysterectomy, and to potentially identify individuals at higher risk who may benefit from vaginal vault testing. METHODS: The authors performed a population-based retrospective study using administrative databases to identify the study population. They identified patients who between 2005-2015 underwent hysterectomy after cervival intraepithelial neoplasia 3+ (CIN3+)/adenocarcinoma in situ (AIS) and calculated the rate of VAIN2 and invasive vaginal cancer. Exclusion criteria are as follows: <21 years and CIN3+ diagnosis prior to 2005. Patients were followed until 2021. RESULTS: During the study period, 6,230 patients underwent hysterectomy for a diagnosis of CIN3+/AIS. The subsequent rates of VaIN2/3, in situ, and invasive cancer were 1.9% (119/6,230) and 0.3 (18/6,230), respectively. Of these, most (84.7%) were made within 5 years of hysterectomy and remained stable over time (2.0% in 2005-2009, 2.4% in 2010-2015). However, only 54% of patients had vault cytology after hysterectomy; among these, 8.2% were abnormal of which 22.8% were diagnosed with VAIN2+. In addition, the risk of VaIN2+ was 5.8% when there was residual CIN3+ versus 2.1% when absent. CONCLUSIONS: Individuals with evidence of CIN3+/AIS at time of hysterectomy are at elevated risk of developing VAIN 2+, with the highest risk occurring within 5 years from surgery. Vault cytology within 1 year of surgery will identify most cases of VAIN2+, but further data monitoring and integration of HPV testing will be required to determine this screening strategy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".