Goal Attainment Scaling in Individuals with Upper Limb Spasticity Post Stroke
Bibliographic record
Abstract
Focusing on rehabilitation goals is an effective approach for improving function in individuals with spasticity after stroke. The objectives of this study were to examine and map goals of post-stroke individuals with spasticity using the Goal Attainment Scale (GAS) and International Classification of Functioning, Disability and Health (ICF), and to evaluate the impact of botulinum toxin A (BoNTA) on occupational performance based on the type of rehabilitation goals. Thirty-one patients were recruited from an outpatient spasticity management clinic. Each patient set one goal, was injected with BoNTA in their spastic upper limb muscles and received standard rehabilitation services twice a week for four weeks. Twenty-seven participants achieved the expected level, and four exceeded the expected level of their rehabilitation goals. Fifty-five percent of the goals were related to Activity/Participation, and 45% of the goals were categorized in the Body Structures and Function domain of the ICF. Fifteen goals focused on positioning, while 16 goals focused on (independent) activities of daily living (ADL/IADL). Both the positioning and ADL/IADL groups experienced a reduction in MAS following the administration of BoNTA. The positioning group was older and more impaired. Mapping goals to ICF identifies specific targets for intervention, establishes a common language within the interdisciplinary team and contextualizes the ways disability impacts goals. This study is limited by a relatively small sample size and absence of a functional measure. Further studies can explore the development of goal/item banks to advance the use of GAS for spasticity management. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".