Are Women Who Exit Colposcopy Without Treatment at Elevated Risk for Cervical Cancer?
Bibliographic record
Abstract
OBJECTIVE: This study aims to estimate the risk of cervical cancer and impact of treatment and other factors in women referred for high-grade (HG) and low-grade (LG) cytologic changes and discharged from colposcopy. MATERIALS AND METHODS: A retrospective cohort study identified 14,787 and 41,916 women with a first-time HG and LG cytologic abnormality between 2007 and 2010 and underwent colposcopy within 1 year. Treatment status was determined within the episode of care. Incidence of cervical cancer postcolposcopy was determined up to March 2015. Logistic regression assessed impact of colposcopic care and patient factors on cancer risk. RESULTS: A total of 62% HG and 28.5% LG had treatment. A total of 28% and 37% with HG and LG abnormalities had only 1 colposcopic evaluation. Subsequent cancer incidence in the untreated HG group was 1.1% versus 0.3% in the treated group. For the LG group, cancer rates were 0.08% in both treatment groups. In the HG group, those with initial colposcopy only and no treatment had an elevated risk [adjusted odds ratio = 6.6 (95% CI = 3.9-11)] compared with treatment with multiple follow-ups. Other significant factors were advancing age and no screening postcolposcopy. For the LG group, those with initial colposcopy only were more at risk regardless of treatment [adjusted odds ratio = 3.8 (95% CI = 1.8-8.1)] compared with multiple colposcopies. CONCLUSIONS: Women who are untreated, with index HG cytology, remain at elevated risk for cervical cancer when the colposcopic episode is limited to 1 examination. Centralized programs are required to ensure that such women are not discharged prematurely or lost to follow up from colposcopy and subsequent screening.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".