How to reduce the impact of cervical cancer worldwide: Gaps and priority areas identified through the essential cancer and primary care packages: An analysis of effective interventions
Bibliographic record
Abstract
BACKGROUND: Cervical cancer is a preventable cancer; therefore, countries should provide strategic, evidence-based health services to reduce its incidence and impact on their populations. Two packages of health services that group together all the services related to cervical cancer, the Essential Cancer Package (9 interventions) and the Primary Care Package (5 interventions), are defined in this article with the aim of assessing the global status of the availability of health services and their coverage in 194 countries worldwide. METHODS: The study was based on the 2017 World Health Organization (WHO) Noncommunicable Disease Country Capacity Survey. Although the survey covered multiple noncommunicable diseases, this report examined only those results pertaining to cervical cancer in the 194 WHO member states divided by WHO region and World Bank income. RESULTS: Only 21% of the countries reported providing all 9 interventions of the Essential Cancer Package, with the highest proportions being found in Europe (45.3%) and among high-income countries (HICs; 54.3%). As for the Primary Care Package, only 19.1% of countries provided all 5 interventions, with the highest proportions being found in Europe (39.6%) and among HICs (45.5%). CONCLUSIONS: The complete development and appropriate coverage of each service listed in both the Essential Cancer Package and the Primary Care Package are essential to reduce the impact of cervical cancer worldwide, and they should be integrated into all cancer control planning efforts.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".