The feasibility of Goal Attainment Scaling in adults with Down syndrome: early results from the LIFE‐DSR‐GAS sub‐study
Bibliographic record
Abstract
Abstract Background Adults with Down syndrome (DS) face a markedly increased risk of Alzheimer’s disease (AD). LIFE‐DSR is a 32‐month longitudinal study of adults with DS aiming to characterize and assess change in cognition, behaviour, function, and health. Goal Attainment Scaling (GAS) is a patient‐centric outcome measure. It evaluates change in goals that are meaningful to participants/caregivers and has proven a valuable and sensitive instrument in dementia clinical trials. To assess the feasibility of using GAS in the DS population and to better understand what is most important to them, the LIFE‐DSR‐GAS sub‐study is enrolling and following participants and their caregivers over 16 months. Method Participants with DS and their caregivers who consented to the LIFE‐DSR‐GAS sub‐study took part in an initial goal‐setting interview with a trained GAS rater to identify meaningful goal areas related to the participant’s condition. Change in these goals was then rated at two follow‐up interviews at 3 and 16 months. To aid in goal setting, a menu of 58 common DS‐AD goal areas was developed pre‐study through interviews with expert clinicians and caregivers. A secondary objective is to evaluate this menu in both its comprehensiveness and usefulness as a resource to improve GAS implementation in DS‐AD studies. Result To date, 31 (of 45) participants with DS and their caregivers have enrolled across three U.S. study sites. All participants have set three goals (the recommended minimum). The mean (standard deviation) time to conduct the interviews was 43.2 (7.5) minutes. 20 participants have completed a 3‐month follow‐up interview; the mean time taken was 18.2 (10.4) minutes. Most goals (83/93) set to date were selected from the DS‐AD menu. The most frequent goals concerned aspects of daily function: Household Chores (8 goals), Personal Care and Hygiene (8), and Meal‐Time Preparation and Activities (7). Conclusion Evaluating health outcomes in people with DS is challenging due to varying levels of baseline cognition and function, further complicated by the onset of dementia. There is a critical need for sensitive and valid measures that capture this heterogeneity. Our early data suggest that GAS may offer a feasible solution.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".