Systematic reviews and meta-analyses of benefits and harms of cryotherapy, LEEP, and cold knife conization to treat cervical intraepithelial neoplasia
Bibliographic record
Abstract
BACKGROUND: Cervical intraepithelial neoplasia (CIN) stage 2-3 is a premalignant lesion that can progress to cervical cancer in 10-20 years if untreated. OBJECTIVES: To conduct systematic reviews of randomized and nonrandomized studies for effects of cryotherapy, loop electrosurgical excision procedure (LEEP), and cold knife conization (CKC) as treatment for CIN 2-3. SEARCH STRATEGY: Medline, Embase, and other databases were searched to February 2012 for benefits, and to July 2012 for harms. Additionally, experts were contacted. Keywords for CIN, cervical cancer, and the treatments were used. SELECTION CRITERIA: Studies of nonpregnant women 18 years or older not previously treated for CIN were included. DATA COLLECTION AND ANALYSIS: Two investigators independently screened and collected data. Relative risks and proportions were calculated and evidence assessed using GRADE (Grading of Recommendations Assessment, Development and Evaluation). MAIN RESULTS: Recurrence rate was 5.3% 12 months after cryotherapy or LEEP, and 1.4% after CKC. There seemed to be little or no differences in frequency of complications after LEEP or cryotherapy, but they occurred more often after CKC. Evidence suggests premature delivery is most common with CKC, but it also occurs after LEEP and cryotherapy. CONCLUSIONS: Despite a comprehensive search, there is very low quality evidence and often no evidence for important outcomes, including reproductive outcomes and complications. Studies assessing these outcomes are needed.
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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 | 0.022 | 0.077 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.040 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".