Selecting Goals and Target Muscles for Botulinum Toxin A Injection Using the Goal Oriented Facilitated Approach to Spasticity Treatment (GO-FAST) Tool
Bibliographic record
Abstract
The objective of this article is to introduce the GO-FAST Tool (developed by the Toxnet group) to clinicians working in the field of neurological rehabilitation, specifically post-stroke spasticity management. The concepts utilized in the Tool and described in this article can be broadly grouped into five topics: the principles of patient-centred goal-setting; an algorithm for setting SMART (specific, measurable, attainable, realistic, and timed) treatment goals; goal-related target muscles and botulinum toxin type A dose determinants; goal attainment follow-up, scoring, and interpretation; and the multimodal approach to spasticity management. The Tool can enhance clinical practice by providing guided assistance with goal-setting and target muscle selection for botulinum toxin type A treatment. It also provides support with the follow-up evaluation of goal attainment and calculation of treatment success. The Tool is designed to be used by clinicians with varying levels of expertise in the field of neurological rehabilitation and post-stroke spasticity management, from those who are new to the field to those with many years of experience. A case study is presented in the Results Section of the article to illustrate the utility of the Tool in setting SMART treatment goals in the management of patients with post-stroke spasticity.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".