Intellectual and Developmental Disabilities (IDD) and Cancer Symptom Reporting in Ontario, Canada
Bibliographic record
Abstract
Introduction: Symptom assessment is key to managing symptom burden following a cancer diagnosis. People with IDD receive inequitable health care and experience worse outcomes from cancer; disparities may also exist in routine cancer symptom screening. In this study, we investigated whether differences exist in cancer symptom assessment between people with and without IDD. Methods: We conducted a matched retrospective cohort of adults in Ontario with and without IDD who received a cancer diagnosis between 2010-2019 using administrative health data at ICES. Individuals were followed until 30/9/2021. Among people with cancer, those with IDD were hard-matched 1:5 to those without IDD on age at diagnosis, sex, diagnosis year, cancer type, and regional cancer centre registration. Cumulative incidence of first symptom assessment accounting for death as a competing risk was estimated. Subdistribution and cause-specific hazard models were used. Effect modification by cancer stage was investigated. Results:1545 people with IDD were matched to 7,725 people without IDD. Individuals with IDD experienced a lower incidence of cancer symptom assessment (1-year probability: 0.62 vs. 0.77). People with IDD had lesser rates of symptom assessment (subdistribution HR: 0.63, 95% CI: 0.59,0.67) (cause-specific HR: 0.69, 95% CI: 0.65,0.73) relative to those without IDD. Results were consistent across cancer stages. Discussion: The incidence of cancer symptom assessment is lower among cancer patients with IDD compared to those without. These findings may indicate poor usability of the symptom screening tool; language and readability checks should be conducted to enhance accessibility of this tool.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".