Combining Human Papillomavirus Testing or Cervicography With Cytology to Detect Cervical Neoplasia
Bibliographic record
Abstract
Abstract Context. —Cervicography and oncogenic human papillomavirus (HPV) testing have been proposed for improving the accuracy of cervical cancer screening. Objective. —To examine whether cervicography and HPV testing can improve beyond chance the detection of cervical intraepithelial neoplasia (CIN) 2 or 3 in women with atypical cells of undetermined significance or low-grade squamous intraepithelial lesions on cytology. Design. —Cross-sectional analysis. Oncogenic HPV testing by Hybrid Capture II assay or cervicography combined with cytology was compared with the reference standard of colposcopy with directed biopsy. Setting. —Community family practices. Participants. —Three hundred four women with low-grade cytologic abnormality. Main Outcome Measures. —The gain in accuracy for detecting histologic CIN 2 or 3 or carcinoma. Because an adjunct test may improve sensitivity by chance alone, the sensitivity or specificity if the second test performed randomly was estimated. Results. —Cervical intraepithelial neoplasia 2 or 3 was found in 11.8% (36/304) of the women and invasive squamous cell carcinoma in 0.3% (1/304). The sensitivity of cytology for detecting CIN 2 or 3 was 73.0% and increased by 21.6% to 94.6% with the addition of a cervigram showing a low-grade lesion or higher or a positive HPV test result. These gains were reduced to 8.1% and 10.8% above the sensitivities expected if the additional tests performed randomly. The corresponding specificities decreased from 49.1% to 32.2% and 33.0%. There was insufficient power to determine whether observed sensitivities were statistically significantly higher than the expected sensitivities. Conclusion. —Adjunctive HPV testing or cervicography may provide similar gains in sensitivity, but they can appear misleadingly large if chance increases are not taken into account.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".