COVID-19 as a catalyst for reimagining cervical cancer prevention
Bibliographic record
Abstract
Cervical cancer has killed millions of women over the past decade. In 2019 the World Health Organization launched the Cervical Cancer Elimination Strategy, which included ambitious targets for vaccination, screening, and treatment. The COVID-19 pandemic disrupted progress on the strategy, but lessons learned during the pandemic - especially in vaccination, self-administered testing, and coordinated mobilization on a global scale - may help with efforts to achieve its targets. However, we must also learn from the failure of the COVID-19 response to include adequate representation of global voices. Efforts to eliminate cervical cancer will only succeed if those countries most affected are involved from the very start of planning. In this article we summarize innovations and highlight missed opportunities in the COVID response, and make recommendations to leverage the COVID experience to accelerate the elimination of cervical cancer globally.
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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 | 0.030 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
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".