Risk of Recurrence After Treatment for Cervical Intraepithelial Neoplasia 3 and Adenocarcinoma In Situ of the Cervix: Recurrence of CIN 3 and AIS of Cervix
Bibliographic record
Abstract
OBJECTIVES: The aim of the study was to evaluate recurrence risk of cervical intraepithelial neoplasia (CIN) 3+ and adenocarcinoma in situ (AIS)+ in a large population cohort of women previously treated for CIN 3/AIS. METHODS: Merging administrative databases with information on health services utilization and jurisdictional cancer registry, we identified all women undergoing treatment for CIN 3 or AIS from 2006 to 2010. Recurrence rate 1-5 years after treatment was defined as a biopsy finding of CIN 3/AIS or retreatment (loop electrosurgical excision procedure [LEEP], laser, cone, hysterectomy). Logistic regression was used to determine odds of recurrence. RESULTS: A total of 15,177 women underwent treatment for CIN 3 (n = 14,668) and AIS (n = 509). The recurrence rate for 5 years was greater for AIS (9.0%) compared with CIN 3 (6.1%). In a multivariate analysis, increased risk of recurrence was shown for age older than 45 years (hazard ratio (HR) = 1.3, 95% CI = 1.1-1.6), AIS compared with CIN 3 (HR = 2.2, 95% CI = 1.5-3.5) first cytology after treatment showing high grade (HR = 12.4, 95% CI = 9.7-15.7), and no normal Pap smears after treatment (HR = 2.8, 95% CI = 2.2-3.7). There was no difference in recurrence risk with treatment type (cone vs LEEP: HR = 1.0, 95% CI = 0.8-1.2, and laser vs LEEP: HR = 1.1, 95% CI = 0.8-1.4) or number of procedures per year performed by physicians (<40 vs >40 procedures: HR = 1.1, 95% CI = 0.9-1.3). CONCLUSIONS: Recurrence risk of CIN 3 and AIS is related to age, histology, and posttreatment cytology, which should assist with discharge planning from colposcopy. Definitive treatment with hysterectomy should be considered in women older than 45 years with additional risk factors for recurrence.
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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 | 0.003 |
| 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 | 0.000 | 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".