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Record W4360823638 · doi:10.1159/000529984

Suitability of Goal Attainment Scaling in Older Adult Populations with Neurodegenerative Disease Experiencing Cognitive Impairment: A Systematic Review and Meta-Analysis

2023· review· en· W4360823638 on OpenAlexaff
Ollie Fegter, Haylie Santos, Alfred Rademaker, Angela Roberts, Emily Rogalskı

Bibliographic record

VenueGerontology · 2023
Typereview
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsWestern University
FundersNational Center for Advancing Translational SciencesNational Institute on AgingNational Institutes of Health
KeywordsPsycINFOCINAHLMEDLINEMeta-analysisSystematic reviewDementiaGoal Attainment ScalingCognitionDiseasePsychologyClinical psychologyGerontologyMedicineIntervention (counseling)PsychiatryPsychological interventionInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Identifying responsive outcome measures for assessing functional change related to cognition, communication, and quality of life for individuals with neurodegenerative disease is important for intervention design and clinical care. Goal Attainment Scaling (GAS) has been used as an outcome measure to formally develop and systematically measure incremental progress toward functional, patient-centered goals in clinical settings. Evidence suggests that GAS is reliable and feasible for use in older adult populations and in adult populations with cognitive impairment, but no review has assessed the suitability of GAS in older adults with neurodegenerative disease experiencing dementia or cognitive impairment, based on responsiveness. This study conducted a systematic review to evaluate the suitability of GAS as an outcome measure for older adult populations with neurodegenerative disease experiencing dementia or cognitive impairment, based on responsiveness. METHODS: The review was registered with PROSPERO and performed by searching ten electronic scientific databases (PubMed, Medline OVID, CINAHL, Cochrane, Embase, Web of Science, PsycINFO, Scopus, OTSeeker, REHABDATA) and four registries (<ext-link ext-link-type="uri" xlink:href="http://Clinicaltrials.gov" xmlns:xlink="http://www.w3.org/1999/xlink">Clinicaltrials.gov</ext-link>, Grey Literature Report, Mednar, OpenGrey). A summary measure of responsiveness (post-intervention minus pre-intervention mean GAS T-score) was compared across eligible studies using a random-effects meta-analysis. Risk of bias in included studies was assessed using the NIH Quality Assessment Tool for Before-After (Pre-Post) Studies with No Control Group. RESULTS: 882 eligible articles were identified and screened by two independent reviewers. Ten studies met inclusion criteria for the final analysis. Of the ten included reports, 3 focus on all-cause dementia, 3 on multiple sclerosis, 1 on Parkinson's disease, 1 on mild cognitive impairment, 1 on Alzheimer's disease, and 1 on primary progressive aphasia. Responsiveness analyses showed pre- and post-intervention GAS goals were significantly different from zero (Z = 7.48, p &lt; 0.001), with post-intervention GAS scores being higher than pre-intervention GAS scores. Three included studies showed a high risk of bias, 3 showed a moderate risk of bias, and 4 showed a low risk of bias. Overall risk of bias of included studies was rated as moderate. CONCLUSION: GAS showed an improvement in goal attainment across different dementia patient populations and intervention types. The overall moderate risk of bias suggests that while bias is present across included studies (e.g., small sample size, unblinded assessors), the observed effect likely represents the true effect. This suggests that GAS is responsive to functional change and may be suitable for use in older adult populations with neurodegenerative disease experiencing dementia or cognitive impairment.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.878
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0080.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.147
GPT teacher head0.434
Teacher spread0.287 · 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 teacher head, not a consensus.

Study designMeta-analysis
Domainnot available
GenreReview

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

Citations5
Published2023
Admission routes1
Has abstractyes

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