Loop electrosurgical excision procedure for the treatment of cervical intraepithelial neoplasia: How much excision is enough?
Bibliographic record
Abstract
This is a retrospective observational study to compare outcomes in patients with cervical intraepithelial neoplasia (CIN) treated with loop electrosurgical excision procedure (LEEP) using combined ectocervical/endocervical resection vs ectocervical resection alone. We demonstrated that additional endocervical resection during loop electrosurgical excision procedure did not significantly lower the risk of subsequent recurrence compared with ectocervical resection alone, in the treatment of CIN. With current published data supporting subsequent increased adverse effects of LEEP on future obstetrical outcomes, endocervical excision should be applied selectively. We recommend that additional endocervical excision should be reserved only for patients with a strong suspicion of underlying endocervical canal involvement based on colposcopic assessment or in patients with unsatisfactory colposcopy, where it is essential to evaluate the endocervical canal.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".