The Effect of Surgeon Volume on the Outcome of Laser Vaporization: A Single-Center Retrospective Study
Bibliographic record
Abstract
Although laser vaporization is a popular minimally invasive treatment for cervical intraepithelial neoplasia (CIN), factors influencing CIN recurrence are understudied. Moreover, the effect of surgeon volume on patients' prognosis after laser vaporization for CIN is unknown. This single-center retrospective study evaluated the predictive value of surgeon volume and patient characteristics for laser vaporization outcomes in women with pathologically confirmed CIN2. Histologically confirmed CIN2 or higher grade after laser vaporization was defined as persistent or recurrent. Various patient characteristics were compared between women with and those without recurrence to examine the predictive factors for laser vaporization. There were 270 patients with a median age of 36 (18-60) years. The median follow-up period was 25 (6-75.5) months and the median period between treatment and persistence or recurrence was 17 (1.5-69) months. The median annual number of procedures for all seven surgeons was 7.8. There were 38 patients (14.1%) with persistent or recurrent lesions-24 had CIN2, 13 had CIN3, and one had adenocarcinoma in situ. Patient age, body mass index, surgeon volume, and history of prior CIN treatment or invasive cervical cancer were not significantly correlated with lesion persistence or recurrence. In conclusion, laser vaporization has comparable success rates and is a feasible treatment for both low- and high-volume surgeons.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".