Efficacy, Safety, and Acceptability of Thermal Coagulation to Treat Cervical Intraepithelial Neoplasia: Pooled Data From Bangladesh, Brazil and India
Bibliographic record
Abstract
Background: Treatment of cervical intraepithelial neoplasia (CIN) using thermal coagulation has recently attracted interest among the medical community in view of the easily portable and light equipment, less treatment time, faster patient turnover, less discomfort, use of minimal amounts of electricity as consumable, less vaginal discharge following treatment and a similar efficacy in treatment of ectocervical CIN lesions as compared to cryotherapy. However, literature on its performance is scarce particularly from low- and middle-income settings. Here, we report the effectiveness, safety and acceptability of thermal coagulation in women treated for histologically proven ectocervical CIN. Methods: We pooled data from five sites in Asia and South America for women treated for CIN with thermal coagulation from March 2010 to October 2015, and followed up within 6 - 12 months after treatment. Estimates of cure, adverse effects, or complications were presented as proportions. Bayesian models were used to assess factors affecting compliance to follow-up and cure rates. Results: Of the 1,626 women treated for CIN at baseline, 775 (48%) had follow-up evaluation. Attendance for follow-up increased with increasing education and CIN grade, and was less likely to be among those aged ≥ 40 years. The estimates of the cure after thermal coagulation treatment were 88% (475/543) for CIN 1, 83% (113/137) for CIN 2 and 83% (79/95) for CIN 3 lesions. No serious adverse effects or complications were observed throughout the follow-up period for which hospitalization was required. Conclusion: Thermal coagulation was effective, safe and accepted in treatment of women diagnosed with CIN. It should be used in the single-visit "screen-and-treat" approach, "see-and-treat" approach and in management of ectocervical CIN in cervical cancer control programs. J Clin Gynecol Obstet. 2017;6(3-4):58-64 doi: https://doi.org/10.14740/jcgo464w
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".