Measuring Colposcopy Quality in Canada: Development of Population-Based Indicators
Bibliographic record
Abstract
Background: Colposcopy is a key part of cervical cancer control. As cervical cancer screening and prevention strategies evolve, monitoring colposcopy performance will become even more critical. In the present paper, we describe population-based colposcopy quality indicators that are recommended for ongoing measurement by cervical cancer screening programs in Canada. Methods: The Pan-Canadian Cervical Cancer Screening Network established a multidisciplinary expert working group to identify population-based colposcopy quality indicators. A systematic literature review was conducted to ascertain existing population and program-level colposcopy quality indicators. A systems-level cervical cancer screening pathway describing each step from an abnormal screening test, to colposcopy, and back to screening was developed. Indicators from the literature were assigned a place on the pathway to ensure that all steps were measured. A prioritization matrix scoring system was used to score each indicator based on predetermined criteria. Proposed colposcopy quality indicators were shared with provincial and territorial screening programs and subsequently revised. Results: The 10 population-based colposcopy quality indicators identified as priorities were colposcopy uptake, histologic investigation (biopsy) rate, colposcopy referral rate, failure to attend colposcopy, treatment frequency in women 18-24 years of age, re-treatment proportion, colposcopy exit-test proportion, histologic investigation (biopsy) frequency after low-grade Pap test results, length of colposcopy episode of care, and operating room treatment rate. Two descriptive indicators were also identified: colposcopist volume and number of colposcopists per capita. Summary: High-quality colposcopy services are an essential component of provincial cervical cancer screening programs. The proposed quality and descriptive indicators will permit colposcopy outcomes to be compared between provinces and across Canada so as to identify opportunities for improving colposcopy services.
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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 | 0.022 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.016 | 0.027 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".