Bibliographic record
Abstract
Upper limb spasticity can interfere with function and cause pain and contractures. Botulinum neuro toxin A (BoNTA) has been effectively used to reduce spasticity; however, the impact of BoNTA combined with rehabilitation on upper limb function is not clear. One possible reason could be the lack of sensitive clinical assessments to show the changes in the focal muscles after an intervention. Therefore, this dissertation aimed to examine the impact of BoNTA combined with rehabilitation on arm function using sensitive, objective assessments in addition to clinical measures. The present findings affirmed that a combination of BoNTA and one month of upper limb rehabilitation assisted patients after stroke with upper limb spasticity to achieve their goals using the Goal Attainment Scale (GAS). Because there is subjectivity in clinical measures such as the GAS, a Kinematic Upper Limb Spasticity Management (KUSA) protocol was developed to objectively characterise the wrist, elbow and shoulder movements that were identified through the goals selected by patients. Kinematic variables including speed, active range of motion and compensatory trunk movement through KUSA were able to distinguish between affected and unaffected sides. Furthermore, KUSA provided supplementary information for motion characterisation that was not available through clinical measures alone. Further, both clinical and kinematic measures were used to examine the effect of two different phases: upper limb rehabilitation, and BoNTA plus rehabilitation on arm function. It was shown that only two clinical measures (Modified Ashworth Scale and Chedoke Arm and Hand Activity Inventory) significantly changed after BoNTA and rehabilitation; however, no changes in kinematic measures were found. Secondary analysis demonstrated that patients with higher motor recovery on the Chedoke McMaster Stroke Assessment improved in all kinematic variables compared to those individuals with lower initial motor recovery stage. This thesis advances knowledge about the types of assessments that identify change in function following spasticity management intervention and characteristics of patients that best responded to this intervention based on these outcomes. These results would be useful in guiding future research on the effectiveness of BoNTA.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".