Traumatic Brain Injury Incidence in Adults with Intellectual and Developmental Disabilities
Bibliographic record
Abstract
BACKGROUND: In Ontario, there are approximately 66,000 adults living with a diagnosis of intellectual and developmental disabilities (IDD). These individuals are nearly twice as likely to experience an injury compared to the general population. Falls are an important contributor to injuries in persons with IDD and in the general population, and are consistently found to be the leading cause of traumatic brain injury (TBI). There is currently no literature that quantitatively examines TBI among persons with IDD. The purpose of this study was to compare the risk of TBI for adults with and without IDD in Ontario over time and by demographic information. METHODS: Using administrative health databases, two main cohorts were identified: (1) adults with IDD, and (2) a random 10% sample of adults without IDD. Within each cohort, annual crude and adjusted incidence of TBI were calculated among unique individuals for each fiscal year from April 1, 2002 to March 31, 2017. RESULTS: Over the 15-year study period, the average annual adjusted incidence of TBI was approximately 2.8 new cases per 1000 among Ontario adults with IDD, compared to approximately 1.53 per 1000 among those without IDD. In both cohorts, a higher proportion of TBI cases were younger (19-29 years) and male. CONCLUSIONS: During the study period, persons with IDD experienced a significantly higher risk of TBI compared to the general population indicating the possibility, and need, for targeted TBI prevention.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".