The Pap Test at Follow-up Colposcopy Examinations: Usefulness in the Enhanced Detection of Cervical Neoplasia
Bibliographic record
Abstract
OBJECTIVE: The frequency at which results of the Pap test performed at follow-up colposcopy examinations were more abnormal than the concurrent tissue specimens and the frequency at which the Pap result gain in high-grade squamous intraepithelial lesion (HSIL) resulted in the increased detection of cervical intraepithelial neoplasia 2,3 were investigated. MATERIALS AND METHODS: Pap test and concurrent tissue samples obtained at all follow-up colposcopy examinations in the year 2000 were coded for comparability of results and were ranked in ascending order from normal to malignant. The frequency at which the Pap test results were more abnormal than the concurrent tissue results was calculated. Results of subsequent cervical investigations were retrieved for all with a Pap test gain in diagnosis of HSIL. RESULTS: In 35% of 2,902 examinations (n = 1,027), the Pap test results were more abnormal than the tissue specimens. The gain in diagnosis was HSIL in 2% (63 of 2,902) and low-grade squamous intraepithelial lesion or better in 33% (964 of 2,902). Among those with a gain in HSIL, cervical intraepithelial neoplasia 2,3 or cancer was tissue-confirmed in 28 samples, for a health benefit of the Pap test of 1.0% (28 of 2,902). CONCLUSIONS: The usefulness of the Pap test performed at follow-up colposcopy examinations for the enhanced detection of CIN 2,3 in this cohort is minimal, and the practice safely could be discontinued.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".