Electrofulguration for Low-Grade Squamous Intraepithelial Lesions of the Cervix (CIN 1)
Bibliographic record
Abstract
OBJECTIVES: To evaluate the therapeutic efficacy and adverse events of electrofulguration for the treatment of cervical intraepithelial lesion grade 1 (CIN 1). MATERIALS AND METHODS: Women aged 19 years and older received electrofulguration for histologically proven exocervical CIN 1. They were followed up at 3, 6, and 12 months with cytologic analysis, colposcopy, and, when indicated, histologic examination. Therapeutic success was defined as absence of disease at 12 months after therapy. Adverse effects were recorded during and after the procedure. RESULTS: Of 78 women treated, 32 (41.0%) were lost to follow-up and 6 patients (7.7%) were retreated. Using intent-to-treat definition including patients lost to follow-up, 40 of 78 (52.6%) were free of disease at the 1-year follow-up visit. Excluding patients lost to follow-up, cure rates were 87% (40/46) and 100% (40/40) after single and repeat treatment, respectively. Adverse effects included mild to moderate discomfort (78%), intermittent spotting (70%), and pelvic pain (44%). The procedure lasted 2 minutes in 83% of cases and was easy to perform. CONCLUSIONS: Electrofulguration under local anesthesia is an attractive ablative treatment method for exocervical CIN 1 and compares favorably with cryocoagulation as per review of the literature.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".