Bibliographic record
Abstract
OBJECTIVE: To evaluate the role of endocervical curettage (ECC) in the diagnosis of cervical intraepithelial neoplasia. MATERIALS AND METHODS: Retrospectively we studied 581 patients who had ECC, 43 (7.4%) had cervical intraepithelial lesions (CIN) 1 ECC, 23 (4.0%) CIN 2-3 ECC, and 515 negative ECC (88.6%). Analysis of variance was used to compare for age and parity, and Pearson's chi-square test was used to analyze the association with other variables such as cytology, images, acetowhite epithelium, microbiopsy, and ECC. Significance level was set at p = 0.05. RESULTS: Age for CIN 1 ECC was at 32.3 (16-66) years; parity was at 0.82 (parity 0-7) compared with 35.2 (18-70) years and parity at 1.52 (parity 0-12) for CIN 2-3 ECC, and 36.1 (14-68) years, parity at 1.1 (parity 0-10) for negative ECC. ECC is associated with Cytobrush cytology (Zelsmyr Cytobrush, International Cytobrush Inc., Hollywood. FL) (p = 0.000) in CIN 2-3 ECC and high-grade squamous intraepithelial lesion (HGSIL) cytology. Positive ECC was not overrepresented in unsatisfactory colposcopy (14/61, 23%) compared with negative ECC (158/526, 30%, p = 0.43). If positive ECC is not associated with the presence of significant acetowhite epithelium, a net association (p = 0.000) was observed between CIN 1 microbiopsies and CIN 1 ECC 9 (12/19), and CIN 2-3 biopsies and CIN 2-3 ECC (12/17). Conization for CIN 2-3 ECC (n = 23) yielded 15 CIN 2-3, two CIN 1, one microinvasive cervical cancer, one cancer of the cervix, and four negative cones. CONCLUSIONS: Positive endocervical curettage is associated with endocervical cytology and microbiopsy. In ablative treatments, when low-grade squamous intraepithelial lesion (LGSIL) smear, satisfactory colposcopy, and CIN 1 biopsy is observed, ECC appears unnecessary since CIN 2-3 ECC was not observed in these patients. All other cases should have ECC prior to ablative therapy. CIN 2-3 ECC, commands conization, in order to eliminate invasive cancer, and confirm and treat CIN 2-3. ▪.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".