Cytologic Predictors of Cervical Intraepithelial Neoplasia in Women with an ASCUS Pap Smear
Bibliographic record
Abstract
OBJECTIVE: To identify cytologic parameters on Pap smears of women with an atypical squamous cells of undetermined significance (ASCUS) diagnosis that could help cytologists to indicate whether a particular ASCUS case is most likely related to cervical intraepithelial neoplasia (CIN) grade 1 or 2/3. STUDY DESIGN: A total of 360 eligible women diagnosed with ASCUS and referred to the colposcopy clinic of Saint-Sacrement Hospital participated in the study. Eligible women were those aged 18-50 years, newly diagnosed with ASCUS, with no history of cervical biopsies or treatment, and not pregnant at the time of the visit. Colposcopically directed biopsies of lesions were obtained. All Pap smears were reviewed according to 36 different cytomorphologic criteria. The regression logistic model was used to estimate the odds ratios (ORs) for the associations between cytologic criteria observed in smears and the diagnosis of CIN made on biopsies. All cytologic criteria significantly (P < .05) associated with CIN were entered in the models, and a backward selection was done to determine independent cytologic predictors of CIN 1 and 2/3. RESULTS: Biopsies revealed that 22.2% of the study population had concurrent CIN. CIN I and 2/3 were identified in 61 (16.9%) and 19 women (5.3%), respectively. Clear perinuclear spaces (OR = 2.5, P = .002) and moderate nuclear atypia (OR = 4.4, P = .02) were two cytologic criteria independently associated with CIN 1. Four independent predictors of CIN 2/3 were identified: the presence of clear perinuclear spaces (OR = 5.9, P = .004), hyperchromasia (OR = 3.9, P = .04), moderate anisokaryosis (OR = 13.1, P = .01 and increased nuclear volume of metaplastic cells (OR = 5.1, P = .007). CONCLUSION: These observations may help cytologists to better categorize ASCUS lesions as intraepithelial ones and will also contribute to improving the Bethesda definition of ASCUS. Further studies are planned to validate these observations.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".