Clinicopathological Determinants of Vaginal and Premalignant-Malignant Cervico-Vaginal Polyps of the Lower Female Genital Tract
Bibliographic record
Abstract
OBJECTIVE: To establish pathology frequencies for polyps in the vagina and cervix and to identify determinants of vaginal polyps and premalignant-malignant cervico-vaginal polyps. MATERIALS AND METHODS: The pathology reports of all cervico-vaginal polyps examined for 6 years at a single institution were classified, and frequencies were calculated. In 2 separate case-control studies, clinical and pathological variables of vaginal cases were compared with cervical controls, and premalignant-malignant cases were compared with benign controls. Differences in variables that were abstracted from patients' chart and pathology reviews were tested for significance. RESULTS: There were 4,402 polyps; 4,340 cervical (98.6%), 62 vaginal (1.4%), 4,268 benign (97.0%), 62 premalignant-malignant (1.4%) (CIN, endometrial hyperplasia, and carcinoma), and 72 unsatisfactory (1.6%). Age was a significant determinant of vaginal and premalignant-malignant polyps, and polyp size and number were significant determinants of vaginal polyps. Vaginal polyps were 3 times more frequent among women 40 years and younger and twice as frequent among those 60 years and older (p < .001). Premalignant-malignant polyps were twice as frequent among women 40 years and younger (p = .04), and malignant polyps were 6 times more frequent in those 60 years and older, which approached statistical significance (p = .09). Multiple polyps were 2 times more frequent (p = .04) and greater than 2 cm was 4 times (p = .01) more frequent among vaginal polyps. CONCLUSIONS: Vaginal polyps and premalignant-malignant cervico-vaginal polyps are rare. Vaginal polyps are larger and multiple and cervico-vaginal polyps in younger and older women are at greater risk of premalignant-malignant changes.
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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 | 0.000 | 0.005 |
| 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".