Comparative Risk of High-Grade Histopathology Diagnosis After a CIN 1 Finding in Endocervical Curettage Versus Cervical Biopsy
Bibliographic record
Abstract
OBJECTIVE: No evidence-based clinical management recommendations exist for women with an endocervical curettage (ECC) cervical intraepithelial neoplasia grade 1 (CIN 1) result when the concurrent cervical biopsy is not high-grade. For women with these pathologic findings, we assessed their short-term risk of high-grade histopathologic diagnosis in the Calgary Health Region where ECC was routinely performed. MATERIALS AND METHODS: We analyzed pathology and colposcopy reports from 1,902 referral colposcopies where both ECC and biopsies were normal or CIN 1. We calculated the short-term risk of CIN 2 or more severe (CIN 2+) detected 12 to 24 months after colposcopy. Pearson χ tests or Fisher exact tests were used to compare risks of a CIN 2+ diagnosis between combinations of test results and strata of risk factors. RESULTS: The short-term risk of CIN 2+ was the same after a CIN 1 biopsy and CIN 1 ECC (4.9% of 1,389 vs 5.0% of 359, respectively, p = .37). Compared with low-grade referral cytology, the risk of CIN 2+ after high-grade cytology was elevated significantly for CIN 1 ECC (13.3% vs 3.3%, p < .01) and nonsignificantly for CIN 1 biopsy (7.1% vs 4.6%, p = .12). CONCLUSIONS: After low-grade cytology, the short-term risk of a high-grade histologic diagnosis in women with either CIN 1 ECC or biopsy is equivalent, suggesting similar management. A CIN 1 ECC may warrant different management in the context of high-grade referral cytology.
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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 | 0.003 | 0.014 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".