Outcomes of Conservative Management of High Grade Squamous Intraepithelial Lesions in Young Women
Bibliographic record
Abstract
OBJECTIVE: The aim of the study was to determine regression rates of cervical intraepithelial neoplasia (CIN) 2 and 3 in women younger than 24 years, followed conservatively for up to 24 months. MATERIALS AND METHODS: This is a retrospective chart review of colposcopy patients in clinic database based on the following: (1) younger than 24 years at first visit; (2) first visit January 1, 2010, to May 31, 2013, and at least 1 follow-up visit after diagnosis; (3) histologic diagnosis of CIN2+; and (4) optimal conservative management (observation for up to 24 months or to 24 years, whichever occurred first). Patient information and clinical/pathologic data were extracted from charts to examine patient characteristics and treatment outcomes, CIN2+ regression rates, median times to regression for CIN2 versus CIN3 (Kaplan-Meier survival analysis), and predictors of regression (multivariate logistic regression analysis). RESULTS: A total of 154 women met criteria. The most severe histological diagnoses were CIN2 in 99 (64.3%), CIN3 in 51 (33.1%), and adenocarcinoma in situ in 4 (2.6%). Adenocarcinoma in situ was immediately treated. In follow-up, CIN2 regressed to CIN1 or negative in 74 women (74.7%)-median time to regression, 10.8 months. Cervical intraepithelial neoplasia 3 regressed in 11 women (21.6%)-median time to regression not reached (last follow-up censored at 52.7 months). Cervical intraepithelial neoplasia 2 on biopsy, low grade referral Pap, and younger age predicted regression. Overall, 49 women (31.8%) were treated. CONCLUSIONS: Conservative management should continue to be recommended to young women with CIN2. Rigorous retention mechanisms are required to ensure that these women return for follow-up.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".