Cervical Cancer Screening Cascade: A Framework for Monitoring Uptake and Retention Along the Screening and Treatment Pathway
Bibliographic record
Abstract
Background: Cervical cancer is a major global health concern, causing approximately 350,000 deaths annually. It is also preventable through effective prevention and early detection. To facilitate elimination, the World Health Organization (WHO) set targets for HPV vaccination, screening, and treatment. Achieving these goals requires frameworks to monitor screening program performance. As many regions transition to HPV primary screening, a standardized Cervical Cancer Screening Cascade can track performance, identify gaps in follow-up, and optimize resource allocation. Methods: This paper introduces a structured cascade developed to monitor uptake, retention, and outcomes in HPV-based screening programs. The Cascade was created through collaboration between public health experts, clinicians, and researchers at the University of British Columbia (UBC), the Women’s Health Research Institute, and BC Cancer. Results: The Cascade outlines four phases: screening, triage, detection, and treatment. Each phase includes two substages: “uptake” and “results,” with an additional substage in screening (“invitation”). “Screening” assesses invitation effectiveness and participation. “Triage” tracks follow-up after a positive screen. “Detection” evaluates attendance at diagnostic appointments, and “Treatment” measures the treatment rate for those with precancerous lesions. Conclusions: The Cascade can guide emerging and existing HPV screening programs within Canada and other similarly resourced settings and serve as a benchmark tool for programs to assess their progress towards cervical cancer elimination.
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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 | 0.052 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".