Stage IV breast, colorectal, and lung cancer at diagnosis in adults living with intellectual or developmental disabilities: A population‐based cross‐sectional study
Bibliographic record
Abstract
BACKGROUND: Cancer is a leading cause of death among people living with intellectual or developmental disabilities (IDD). Although studies have documented lower cancer screening rates, there is limited epidemiological evidence quantifying potential diagnostic delays. This study explores the risk of metastatic cancer stage for people with IDD compared to those without IDD among breast (female), colorectal, and lung cancer patients in Canada. METHODS: Separate population-based cross-sectional studies were conducted in Ontario and Manitoba by linking routinely collected data. Breast (female), colorectal, and lung cancer patients were included (Manitoba: 2004-2017; Ontario: 2007-2019). IDD status was identified using established administrative algorithms. Modified Poisson regression with robust error variance models estimated associations between IDD status and the likelihood of being diagnosed with metastatic cancer. Adjusted relative risks were pooled between provinces using random-effects meta-analyses. Potential effect modification was considered. RESULTS: The final cohorts included 115,456, 89,815, and 101,811 breast (female), colorectal, and lung cancer patients, respectively. Breast (female) and colorectal cancer patients with IDD were 1.60 and 1.44 times more likely to have metastatic cancer (stage IV) at diagnosis compared to those without IDD (relative risk [RR], 1.60; 95% confidence interval [CI], 1.16-2.20; RR, 1.44; 95% CI, 1.24-1.67). This increased risk was not observed in lung cancer. Significant effect modification was not observed. CONCLUSIONS: People with IDD were more likely to have stage IV breast and colorectal cancer identified at diagnosis compared to those without IDD. Identifying factors and processes contributing to stage disparities such as lower screening rates and developing strategies to address diagnostic delays is critical.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".