Disability and Participation in Colorectal Cancer Screening: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: The aim of this study is to assess the impact of disability on participation in CRC screening and to determine the overall effect size. METHODS: We conducted a systematic review and meta-analysis to compare CRC screening participation in individuals with and without disabilities. The search encompassed five databases (PubMed, EMBASE, Scopus, Google Scholar, medRxiv). Pooled estimates were calculated for each type of CRC screening and disability categories to synthesize the findings. The participation in CRC screening was derived using a random effects model. RESULTS: A total of 20 articles were included, most of them from the USA. Based on pooled estimates, individuals with disabilities have lower odds of undergoing CRC screening versus those without disabilities (OR = 0.80, 95%CI 0.73-0.87). Analysis by screening type indicated that individuals with a disability have lower odds of a fecal occult blood test or a fecal immunochemical test (OR: 0.72, 95%CI 0.65-0.81), with no significant difference for a colonoscopy. Individuals with intellectual disabilities had significantly lower rates of CRC screening participation (OR = 0.65, 95%CI 0.53-0.79), especially for FOBT/FIT (OR = 0.58, 95%CI 0.49-0.69). CONCLUSIONS: Disparities exist for CRC screening participation in people with disabilities. Further research and coordinated efforts are essential to develop interventions for improving early cancer diagnosis for this non-negligible patient group.
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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 | 0.016 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.033 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".