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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".