Risks for Cervical Intraepithelial Neoplasia 3 Among Adolescents and Young Women With Abnormal Cytology
Bibliographic record
Abstract
OBJECTIVE: To estimate the risks of cervical intraepithelial neoplasia (CIN) 3 among girls and women aged 13 to 24 years who were referred for abnormal cytology while receiving care in a large health maintenance organization. METHODS: At the time of referral, patients had a colposcopic examination and biopsy if needed. Histology was sent to a centralized laboratory. Patients were interviewed for risk behaviors. Data analysis included multinomial logistic regression analysis to compare three groups: CIN 3 to CIN 1 or less, CIN 3 to CIN 2, and CIN 2 to CIN 1 or benign. RESULTS: Cervical intraepithelial neoplasia-3 was found in 6.6% (95% confidence interval [CI] 4.6-8.6%) of the 622 girls and women referred and no cancers were detected. Risk for CIN 3 compared to CIN 1 or less included human papillomavirus 16 or 18 (odds ratio [OR] 30.93, 95% CI 6.95-137.65), high-risk, non-16/18 human papillomavirus (OR 6.3, 95% CI 1.3-29.4), and time on oral contraceptives (OR 1.36 per year of use, 95% CI 1.08-1.71). CONCLUSION: Our data support conservative care for adolescents and young women with abnormal cytology since CIN 3 was rare and cervical cancer was never found. Human papillomavirus 16 or 18 was strongly associated with for CIN 3, and testing for these types may be warranted for triage of abnormal cytology in this age group. LEVEL OF EVIDENCE: II.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 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".