Accuracy of Colposcopy in the Diagnostic Setting Compared With the Screening Setting
Bibliographic record
Abstract
OBJECTIVE: To estimate the accuracy of colposcopy to identify cervical precancer in screening and diagnostic settings. METHODS: As part of a larger clinical trial to evaluate the diagnostic accuracy of optical spectroscopy, we recruited 1,850 patients into a diagnostic or a screening group depending on their history of abnormal findings on Papanicolaou tests. Colposcopic examinations were performed and biopsies specimens obtained from abnormal and normal colposcopic sites for all patients. The criterion standard of test accuracy was the histologic report of biopsies. We calculated sensitivities, specificities, likelihood ratios, receiver operating characteristic curves, and areas under the receiver operating characteristic curves. RESULTS: The prevalence of high-grade squamous intraepithelial lesions (HSIL) or cancer was 29.0% for the diagnostic group and 2.2% for the screening group. Using a disease threshold of HSIL, colposcopy had a sensitivity of 0.983 and a specificity of 0.451 in the diagnostic group when the test threshold was low-grade squamous intraepithelial lesions (LSIL), and a sensitivity of 0.714 and a specificity of 0.813 when the test threshold was HSIL. Using the same HSIL disease threshold, in the screening group, colposcopy had a sensitivity of 0.286 and a specificity of 0.877 when the test threshold was LSIL, and a sensitivity of 0.191 and a specificity of 0.961 when the threshold was HSIL. The colposcopy area under the receiver operating characteristic curve was 0.821 (95% confidence interval 0.79-0.85) in the diagnostic setting compared with 0.587 (95% confidence interval 0.56-0.62) in the screening setting. Changing the disease threshold to LSIL demonstrated similar patterns in the tradeoff of sensitivity and specificity and measure of accuracy. CONCLUSION: Colposcopy performs well in the diagnostic setting and poorly in the screening setting. Colposcopy should not be used to screen for cervical intraepithelial neoplasia. LEVEL OF EVIDENCE: II.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".