A Systematic Review of Risk Factors for Development, Recurrence, and Progression of Vulvar Intraepithelial Neoplasia
Bibliographic record
Abstract
OBJECTIVE: Vulvar intraepithelial neoplasia (VIN) is a premalignant condition with high recurrence rates despite treatment. Vulvar intraepithelial neoplasia develops through separate etiologic pathways relative to the presence or absence of human papillomavirus (HPV) and TP53 mutations. This systematic review was conducted (1) to identify historical risk factors for the development, recurrence, and progression of VIN and (2) to critique these risk factors in the context of advances made in the stratification of VIN based on HPV or TP53 status. MATERIALS AND METHODS: A systematic search was performed on MEDLINE, Embase, Cochrane Database, PsychInfo, and CINAHL from inception to July 5, 2021. Three gynecologic oncologists independently evaluated the eligibility of studies based on predetermined inclusion and exclusion criteria, abstracted data, and then analyzed the relevant data. RESULTS: A total of 1,969 studies (involving 6,983 patients) were identified. Twenty-nine studies met inclusion criteria. The quality of evidence was low; primarily level 2b (Oxford Centre for Evidence-Based Medicine). Risk factors associated with the development of VIN include: smoking and coexisting vulvar dermatoses. Risk factors associated with recurrence include: smoking, multifocal disease, and positive surgical margins. Recent studies identified the presence of differentiated VIN/TP53 mutation as the most significant risk factor for both VIN recurrence and malignant progression. CONCLUSIONS: The current body of evidence consists primarily of small retrospective observational studies. Well-designed retrospective case-control series and/or prospective observational studies are urgently needed. Ideally, future studies will collect standardized data regarding associated risk factors and stratify women with VIN based on HPV and TP53 status.
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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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".