Intellectual and Developmental Disabilities (IDD) and Cancer Symptom Reporting in Ontario, Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it