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Record W7116971122 · doi:10.1002/alz70861_108133

Evaluating the Feasibility of a Personalized Endpoint in Down Syndrome‐associated Alzheimer’s Disease: Inventory‐facilitated Goal Attainment Scaling

2025· article· en· W7116971122 on OpenAlexaff
Günes Sevinc, Michelle George, Katie Crespo, Lois Kelly, Hampus Hillerstrom, James A. Hendrix, Chere A. T. Chapman, K. Rockwood

Bibliographic record

VenueAlzheimer s & Dementia · 2025
Typearticle
Languageen
FieldMedicine
TopicDown syndrome and intellectual disability research
Canadian institutionsNova Scotia Health AuthorityDalhousie University
Fundersnot available
KeywordsGoal Attainment ScalingResource (disambiguation)Scale (ratio)Point (geometry)Scaling

Abstract

fetched live from OpenAlex

BACKGROUND: Evaluating health outcomes in Down syndrome-associated Alzheimer's disease (DS-AD) is challenging due to variability in baseline function and cognition. Personalized outcome assessments, like Goal Attainment Scaling (GAS), can address this gap and capture treatment responses across varying baseline states and symptom manifestations. However, implementation must be standardized through tools such as goal inventories. Here, we assessed the content validity of the DS-AD goal inventory (Knox et al., 2020, 2021) and investigated the feasibility and acceptability of inventory-facilitated GAS as a patient-centric tool to evaluate treatment response. METHOD: We conducted a prospective, 16-month, non-interventional study using the DS-AD goal inventory to facilitate GAS with caregivers of individuals with Down syndrome. The inventory included 58 goal areas distributed across behavior, cognition, daily function, executive function, and physical manifestation domains. The content validity of the goal inventory was assessed through a qualitative analysis of goal scales and alignment with the existing inventory. Goal count, goal scale completeness, and interview durations were used as feasibility indicators. An end-of-study survey evaluated acceptability. RESULT: Forty-six caregivers set 3 goals with 5-levels each and assessed goal attainment at the 3 (n =45) and 16-month follow-ups (n =43). Mean interview times were 38.6 (±10.4) minutes for goal-setting and 17.9 (±9.5) and 14.5 (±4.7) minutes for 3- and 16-month follow-ups. Out of 138 goals, 117 were initially selected from the inventory. The qualitative analysis indicated that the majority of the goals were from the Daily Function (n =65) and Behavior (n =26) domains (Figures 1&2), and that the inventory covered 125 (91%) goals. A qualitative analysis of the remaining goals (n =13) revealed diet and physical activity as additional goal areas. Survey results (n =33) indicated that caregivers had positive experiences with GAS (n =31), found their goals meaningful (n =31), valued improved clinician communication (n =30), and gained new perspectives and knowledge (n =10). CONCLUSION: Our findings indicate GAS is feasible and acceptable to caregivers, and the DS-AD goal inventory comprehensively reflects patient priorities. Additional gaps identified led to inventory enhancements, resulting in a more comprehensive resource to standardize GAS implementation in DS-AD studies.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.022
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.116

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.040
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.149
GPT teacher head0.421
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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