Comparison of Predictors for High-Grade Cervical Intraepithelial Neoplasia in Women with Abnormal Smears
Bibliographic record
Abstract
BACKGROUND: The detection of high-risk human papillomavirus (HPV) DNA provides higher sensitivity but lower specificity than cytology for the identification of high-grade cervical intraepithelial neoplasia (CIN). This study compared the sensitivity and specificity of several adjunctive tests for the detection of high-grade CIN in a population referred to colposcopy because of abnormal cytology. METHODS: 953 women participated in the study. Up to seven tests were carried out on a liquid PreservCyt sample: Hybrid Capture II (Digene), Amplicor (Roche), PreTect HPV-Proofer (NorChip), APTIMA HPV assay (Gen-Probe), Linear Array (Roche), Clinical-Arrays (Genomica), and CINtec p16INK4a Cytology (mtm Laboratories) immunocytochemistry. Sensitivity, specificity, and positive predictive value (PPV) were based on the worst histology seen on either the biopsy or the treatment specimen after central review. RESULTS: 273 (28.6%) women had high-grade disease (CIN2+) on worst histology, with 193 (20.2%) having CIN3+. For the detection of CIN2+, Hybrid Capture II had a sensitivity of 99.6%, specificity of 28.4%, and PPV of 36.1%. Amplicor had a sensitivity of 98.9%, specificity of 21.7%, and PPV of 33.5%. PreTect HPV-Proofer had a sensitivity of 73.6%, specificity of 73.1%, and PPV of 52.0%. APTIMA had a sensitivity of 95.2%, specificity of 42.2%, and PPV of 39.9%. CINtec p16INK4a Cytology had a sensitivity of 83.0%, specificity of 68.7%, and PPV of 52.3%. Linear Array had a sensitivity of 98.2%, specificity of 32.8%, and PPV of 37.7%. Clinical-Arrays had a sensitivity of 80.9%, specificity of 37.1%, and PPV of 33.0%.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".