Interdisciplinary spasticity management clinic outcomes using the Goal Attainment Scale: A retrospective chart review
Bibliographic record
Abstract
OBJECTIVE: To generate practice-based evidence of outcomes in an interdisciplinary spasticity management clinic using practical application of the Goal Attainment Scale (GAS). DESIGN: Retrospective chart review. PATIENTS: A total of 225 adult patients who were referred for spasticity management at a tertiary rehabilitation hospital and returned for follow-up between 2010 and 2013. METHODS: GAS scores were determined for all patients. GAS T-scores were evaluated based on age; sex; diagnosis; International Classification of Functioning, Disability and Health (ICF) domain; body region affected; and site of botulinum neurotoxin injection. RESULTS: The distribution of GAS outcomes did not vary by age, sex or diagnosis. The overall GAS T-score for the clinic was 47.7, which is consistent with appropriate goal setting. GAS T-scores did not vary by diagnosis or ICF domain. Significant intervention effects were identified for botulinum neurotoxin, with improvements in GAS T-scores for treatment targeted to both upper and lower limb muscles, compared with no botulinum neurotoxin, across diagnoses and ICF domains. CONCLUSION: The GAS is a useful patient-centred outcome measure that can be practically applied in the clinical setting for a heterogeneous population with diverse goals. Botulinum neurotoxin treatment in this setting was associated with improved goal attainment relating to multiple ICF domains.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".