Meta‐analysis of the efficacy of cold coagulation as a treatment method for cervical intraepithelial neoplasia: a systematic review
Bibliographic record
Abstract
BACKGROUND: Cold coagulation is an ablative method for treatment of cervical intraepithelial neoplasia (CIN). Despite reports of efficacy against all grades of CIN (CIN1-3), cold coagulation has been infrequently used since the 1980s, and was absent from the recent Cochrane review on CIN treatment. OBJECTIVES: To provide a systematic review of cold coagulation efficacy and acceptability for CIN treatment through meta-analysis of clinical reports and a randomised control trial. SEARCH STRATEGY: A literature search in PubMed, Web of Science, EMBASE, and regional databases yielded 388 papers. Title, abstract and/or reference list review identified 22 papers describing cold coagulation treatment of CIN, with 13 providing adequate data for inclusion in the meta-analysis. SELECTION CRITERIA: Publications or conference abstracts describing original data (number of women treated, followed up and cured, provider type, cure definition) were retained. No language or publication date limitations were imposed. DATA COLLECTION AND ANALYSIS: Data extracted from 13 studies were pooled, and statistical analyses of proportion cured were conducted with data stratified by lesion grade and study region. MAIN RESULTS: Among 4569 CIN patients treated with cold coagulation, summary proportion cured of 96% [95% confidence interval (CI) 92-99%] and 95% (92-98%) were obtained for CIN1 and CIN2-3 disease, respectively. Side-effects and adverse effects were infrequent, and fertility was not impaired. CONCLUSIONS: Cold coagulation CIN cure rates were comparable to those of other excisional and ablative methods. Cold coagulation is indicated for all grades of CIN, is safe, quick and acceptable, and may be of particular relevance for use in resource-limited settings.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| 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 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".