From potential to practice: how accelerating access to HPV tests and screen and treat programmes can help eliminate cervical cancer
Bibliographic record
Abstract
Human papillomavirus (HPV) vaccination campaigns to prevent cervical cancer are being considered and implemented in countries around the world. While vaccination will protect future generations, it will not help the millions of women currently infected, leading to an estimated 311 000 deaths per year globally. This paper examines a selection of strategies that when applied to both existing and new technologies, could accelerate access to HPV testing. Authors from the US Agency for International Development, the National Institutes of Health, and the Bridge to Health Medical and Dental, a non-governmental organisation, joined forces to propose a scalable and country-directed solution for preventing cervical cancer using an end-to-end approach. Collectively, the authors offer seven evidence-based strategies, that when used alone or in combination have the ability to reduce HPV-caused cervical cancer deaths and disability. These strategies include (1) consistent HPV test intervals to decrease HPV DNA test costs; (2) exploring market shaping opportunities; (3) employing iterative user research methodologies like human-centred design; (4) target product profiles for new HPV tests; (5) encouraging innovation around cervical cancer screen and treat programmes; (6) developing national cancer control plans; and (7) integrating cervical cancer screen and treat services into existing infrastructure. By using the strategies outlined here, in combination with HPV vaccination campaigns, national governments will be able to scale and expand cervical cancer screening programmes and provide evidence-based treatment programmes for HPV-infected women.
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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 | 0.037 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".