The accuracy of colposcopic biopsy: Analyses from the placebo arm of the Gardasil clinical trials
Bibliographic record
Abstract
We evaluated the overall agreement between colposcopically directed biopsies and the definitive excisional specimens within the context of three clinical trials. A total of 737 women aged 16-45 who had a cervical biopsy taken within 6 months before their definitive therapy were included. Per-protocol, colposcopists were to also obtain a representative cervical biopsy immediately before definitive therapy. Using adjudicated histological diagnoses, the initial biopsies and the same day biopsies were correlated with the surgically excised specimens. The overall agreement between the biopsies taken within 6 months of definitive therapy, and the definitive therapy diagnoses was 42% (weighted kappa = 0.34) (95% CI: 0.29-0.39). The overall underestimation of cervical intraepithelial neoplasia grade 2/3 or adenocarcinoma in situ (CIN2-3/AIS) and CIN3/AIS was 26 and 42%, respectively. When allowing for one degree of variance in the correlation, the overall agreement was 92% for CIN2-3/AIS. The overall agreement between the same day biopsy and definitive therapy specimen was 56% (weighted kappa = 0.41) (95% CI: 0.36-0.47), and the underestimation of CIN2-3/AIS was 57%. There were significant associations in the agreement between biopsies and excisional specimen diagnoses when patients were stratified by age, number of biopsies, lesion size, presence of human papillomavirus (HPV)16/18 and region. Of 178 diagnostic endocervical curettages performed, 14 (7.9%) found any HPV disease. Colposcopic accuracy improved when CIN2 and CIN3/AIS were grouped as a single predictive measure of high-grade disease. Colposcopy functioned well when allowed a one-degree difference between the biopsy and the surgical histologic interpretations, as done in clinical practice. Taking more than one biopsy improved colposcopic accuracy and could improve patient management.
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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 | 0.185 | 0.305 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".