Defining the Longitudinal Risk of CIN 3+ for <CIN 2 Colposcopy for Patients Referred With High-Grade Cytology
Bibliographic record
Abstract
OBJECTIVE: To determine the baseline and cumulative risk of cervical intraepithelial neoplasia (CIN)3 and invasive cervical cancer in participants referred to colposcopy with high-grade cytology and <CIN2 histology, stratified by biopsy result. METHODS: The authors linked administrative databases including cytology, pathology, cancer registries, and physician billing history to identify participants referred to colposcopy between January 2012 and December 2013 with high-grade cytology (atypical squamous cells [ASC]-H, high-grade squamous intraepithelial lesion [HSIL], invasive squamous cell carcinoma, adenocarcinoma, atypical glandular cells [AGC], adenocarcinoma in situ) and had <CIN2 (with and without biopsy confirmation) at colposcopy. Three- and 5-year risks of CIN3 and invasive cervical cancer were generated using Kaplan-Meier survival analysis. RESULTS: Among 4,168 women referred to colposcopy for ASC-H, HSIL, squamous cell carcinoma, or adenocarcinoma, the 3- and 5-year CIN3 incidence rates were 17.7%/20.0% no biopsy, 13.0%/15.1% negative biopsy, and 18.9%/20.0% low-grade squamous intraepithelial lesion (LSIL) biopsies. The 3- and 5-year incidences of invasive cancer were: 1.25%/1.68% no biopsy, 0.78%/1.04% negative biopsy, and 0%/0% LSIL biopsy. When the initial cytology was AGC/adenocarcinoma in situ (n = 944), the 3- and 5-year rates of CIN3 were 7.42%/8.39% no biopsy, 7.41%/9.26% negative biopsy, and 7.69%/7.69% LSIL biopsy. The invasive cancer rates were 1.12%/1.54% no biopsy, 0.46%/0.46% negative biopsy, and 0.0%/0.0% LSIL biopsy. By screening cytology, participants referred for HSIL had the highest 3- and 5-year rates of CIN3 (18.9% and 21%) compared with AGC (7.22%/8.28%) and ASC-H (15.5%/18%). The 3- and 5-year invasive cancer rates were 1.38%/1.75% HSIL, 0.85%/1.17% AGC, and 0.91%/1.36% ASC-H. CONCLUSIONS: In participants referred for high-grade cytology where colposcopy shows
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".