Treatment Outcomes of Patients With Cervical Intraepithelial Neoplasia or Invasive Carcinoma Who Underwent Loop Electrosurgical Excision Procedure
Bibliographic record
Abstract
BACKGROUND: This study aimed to evaluate the treatment outcomes of cervical intraepithelial neoplasia (CIN) or cancer patients who underwent loop electrosurgical excision procedure (LEEP) in terms of primary outcome and factors associated with persistence/recurrence. METHODS: Patients with CIN or cancer who underwent LEEP from January 2007 to December 2015 were reviewed. Data collected were age, parity, menopausal status, human immunodeficiency virus (HIV) infection, smoking, cervical cytology, histopathology from cervical biopsy and LEEP including margin status, final histopathology, and follow-up data. RESULTS: The mean age of 385 patients was 41.9 ± 10.8 years (range 18 - 79 years). Majority were multiparous (81.6%) and premenopausal (78.2%). There were 15.3% of patients with HIV infection. The most common cervical cytology was high-grade squamous cell intraepithelial lesion (HSIL, 44.1%), followed by atypical squamous cells of undetermined significance (ACS-US, 21%). Minor complications of bleeding or infection from LEEP were encountered in 7.3%. Among 153 patients (39.7%) who had positive margin(s), 43 underwent second LEEP, whereas 76 had hysterectomy. From all patients, 47 had failure after treatment (12.2%), being either persistence (30 patients; 7.8%) or recurrence (17 patients; 4.4%). Factors associated with persistence or recurrence by multivariate analysis were age ≥ 55 years old, HIV infection, final diagnosis of invasive cancer, and positive endocervical margin or both ecto- and endo- cervical margins. CONCLUSIONS: LEEP had low rate of persistence/recurrence. Age ≥ 55 years old, HIV infection, final diagnosis of cancer, and positive endocervical or both endo- and ecto- surgical margin(s) were significantly associated with persistent or recurrent diseases.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".