Evidence supporting see‐and‐treat management of cervical intraepithelial neoplasia: a systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Studies of see-and-treat management of cervical intraepithelial neoplasia (CIN) vary in their inclusion criteria, resulting in a broad range of overtreatment rates. OBJECTIVES: To determine overtreatment rates in see-and-treat management of women referred for colposcopy because of suspected CIN, in order to define circumstances supporting see-and-treat management. SEARCH STRATEGY: MEDLINE, EMBASE, and the Cochrane Library were searched from inception up to 12 May 2014. SELECTION CRITERIA: Studies of see-and-treat management in women with a reported cervical smear result, colposcopic impression, and histology result were included. DATA COLLECTION AND ANALYSIS: Methodological quality was assessed with the Newcastle-Ottawa scale. We used the inverse variance method for pooling incidences, and a random-effects model was used to account for heterogeneity between studies. Overtreatment was defined as treatment in patients with no CIN or CIN1. MAIN RESULTS: Thirteen studies (n = 4611) were included. The overall overtreatment rate in women with a high-grade cervical smear and a high-grade colposcopic impression was 11.6% (95% CI 7.8-15.3%). The overtreatment rate in women with a high-grade cervical smear and low-grade colposcopic impression was 29.3% (95% CI 16.7-41.9%), and in the case of a low-grade smear and high-grade colposcopic impression it was 46.4% (95% CI 15.7-77.1%). In women with a low-grade smear and low-grade colposcopic impression, the overtreatment rate was 72.9% (95% CI 68.1-77.7%). AUTHOR'S CONCLUSIONS: The pooled overtreatment rate in women with a high-grade smear and high-grade colposcopic impression is at least comparable with the two-step procedure, which supports the use of see-and-treat management in this subgroup of women. TWEETABLE ABSTRACT: See-and-treat management is justified in the case of a high-grade smear and a high-grade colposcopic impression.
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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 | 0.019 | 0.055 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.034 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".