The importance of cancer prevention policies to inform and guide preventative and screening measures for people with intellectual disabilities: The COST project “Cancer- Understanding Prevention in Intellectual Disabilities”
Bibliographic record
Abstract
Cancer is a global public health problem, but its exact prevalence in people with intellectual disabilities is still uncertain. This population, with limited health skills and complex health needs, faces many challenges in cancer prevention, screening, timely diagnosis and treatment. Furthermore, they are often underrepresented in general cancer prevention and screening policies across Europe, leading to widened disparities in health outcomes and premature mortality. Thus, unified national and local policies are needed to reduce inequalities and promoting a pan-European inclusion of people with intellectual disabilities. Our goal is to raise public awareness of this issue, including the involvement of people with intellectual disabilities, and promote engagement from relevant stakeholders. The COST Action 'Cancer- Understanding Prevention in Intellectual Disabilities' (CUPID) project will address health inequalities faced by people with intellectual disabilities in relation to cancer, and support the development of policy recommendations specifically tailored to their unique cognitive and healthcare needs, having a positive long-term impact on quality of life.
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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 | 0.048 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".