Experts say that cervical cancer could be eliminated worldwide with screening, human papillomavirus vaccine
Bibliographic record
Abstract
The first study examined how new cervical cancer cases could be eliminated by introducing or increasing HPV vaccination coverage or by combining vaccination levels with cervical cancer screening once or twice in a woman's lifetime.1 The second study evaluated how cervical cancer deaths could be reduced by treatment along with vaccination and screening.2 Both modeled the impact of these interventions in 78 low- and lower-middle-income countries (LMICs) in which the disease is the second most common cancer. WHO established the Cervical Cancer Elimination Modelling Consortium, which conducted the studies to evaluate how successful these strategies would be. The group consists of modeling teams from Université Laval in Quebec, Canada; Harvard TH Chan School of Public Health in Boston, Massachusetts; and Australia's Cancer Council NSW. The first study assessed scenarios that included HPV vaccination of girls, vaccination combined with screening women aged 35 years, and vaccination combined with screening twice in a woman's lifetime. Researchers found that vaccination alone could reduce the number of cervical cancer cases by more than 89%—or 60 million cases—in LMICs. Countries in which incidences are currently more than 25% per 100,000 women would not be able to eliminate the disease with vaccination alone, however; in sub-Saharan Africa, for example, elimination would be possible in only 27% of countries. If twice-per-lifetime screening were scaled up in addition to vaccination, 100% of countries could achieve elimination by reducing cases by 97% and averting 74 million cases by 2120. In addition, this strategy would speed up the disease elimination process by 11 to 31 years. The second study evaluated all 3 of the interventions in WHO's strategy and found that 300,000 deaths, equivalent to a reduction of 34%, could be averted by 2030. Projected to 2070, such an effort would avert 14.6 million deaths and reduce mortality by 92% versus only 62% with vaccination alone. Although researchers say that their findings underscore the need to act immediately on all 3 fronts, they note that these results will be achievable only through a combination of significant international financial aid and political commitment aimed at scaling up vaccination, screening, and treatment. At press time, the studies' recommendations were expected to be considered for approval by WHO.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.043 | 0.010 |
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".