Applying a General Measure of Frailty to Assess the Aging Related Needs of Adults with Intellectual and Developmental Disabilities
Bibliographic record
Abstract
Abstract Adults with intellectual and developmental disabilities often experience premature aging and high levels of frailty. Frailty characterizes health complexities and identifies adults with increased risks for adverse outcomes. This paper compared the prevalence of frailty amongst adults (aged 18–99 years) with and without intellectual and developmental disabilities. Frailty was measured using the Frailty Marker, based on the Adjusted Clinical Groups‐Predicative Model, and was compared between a cohort of 51,138 adults with intellectual and developmental disabilities and a random sample of 3,272,080 adults without intellectual developmental disabilities. Approximately 9% of persons with intellectual and developmental disabilities were frail, compared to only 3% of persons without intellectual and developmental disabilities. Women, older adults, and adults with mental illness or addiction(s), were more likely to be frail. Adults with intellectual and developmental disabilities are increasingly vulnerable as they age. However, to appropriately characterize frailty in this population, measures should be more inclusive of health characteristics and fluctuations that are related to frailty. Future research should investigate alternative measures of frailty for persons with intellectual and developmental disabilities, including measures derived from standardized health assessments, to meet the needs of the aging population.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".