Natural History of Primary Vaginal Intraepithelial Neoplasia With and Without Treatment: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Knowledge about evolution of treated and untreated primary vaginal intraepithelial neoplasia (VaIN) remains limited, as current guidelines recommend treatment. This study investigates the natural history of VaIN based on existing literature. Methods: This study is a systematic review and descriptive meta-analysis. We searched the PubMed, Scopus, Web of Science (WoS), and Scientific Electronic Library Online (SciELO) databases to identify clinical series reporting the no-regression rate(including persistence, recurrence, or progression events) of primary VaIN. We recorded data categorized by VaIN grade and treatment status. Clinical series that reported VaIN grade, follow-up time (median or mean of six months or more), treatment details, and whether treatment was performed were eligible for inclusion. Additionally, some internal hospital databases on VaIN were included. Data were pooled at each follow-up time point, using six-month intervals. From these pooled rates, trend curves were constructed to describe the natural history of treated (various therapies) and untreated low-grade and high-grade VaIN. Results: A total of 150 series were included in the data synthesis. Five subgroups were assessed for low-grade VaIN and twelve for high-grade VaIN. The estimated 5-year no-regression rate of untreated low-grade VaIN, predicted by trend curve, was 14.0% (95% confidence intervals (95% CI): 9.2%–44.0%), indicating that 86.0% of untreated low-grade VaIN would regress within 5-years. The 5-year no-regression rate for untreated high-grade VaIN, also predicted by trend curve, was 14.2% (95% CI: 10.2%–24.8%), indicating that 85.8% of untreated high-grade VaIN regress within 5-years. It cannot be determined to what extent treatment modifies the natural history of VaIN. Current assessments suggest that only low-level evidence is available on VaIN. Conclusion: A large proportion of untreated VaIN lesions, regardless of grade, would resolve after 5 years of follow-up, with at least 14% of lesions unlikely to resolve. Registration: The study has been registered on https://www.crd.york.ac.uk/PROSPERO/view/CRD42023445810 (registration number: CRD42023445810).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".