Economics of dry needling and botulinum toxin type A for treatment of post-stroke spasticity: a review
Bibliographic record
Abstract
Stroke is one of the most common causes of disability and exerts a high burden of direct and indirect costs. Stroke may cause spasticity, which limits patients’ abilities and affects their activities of daily living, decreasing their quality of life. Conventional treatments are based on physical therapy, anti-spasticity medication, and botulinum toxin type A (BTX-A). However, recently, non-pharmacological approaches have been used, such as dry needling (DN) of myofascial trigger points. BTX-A and DN are two treatments that aim to decrease spasticity in patients with stroke, but their mode of action, application, and costs differ. Thus, there is a need to determine the comparative economics of post-stroke spasticity treatments. For this purpose, a search for all types of cost-effectiveness studies (randomized controlled trials, matched controls, and cohorts) and models of epidemiological data was performed. Studies were selected if they included economic outcomes in stroke patients treated with BTX-A or DN. As a result, 7 studies of BTX-A and 2 of DN were selected. Similarities were found in the outcomes used to assess the effectiveness of both treatments in most studies, with modifications of the Ashworth Scale [Modified Ashworth Scale (MAS)/Modified Modified Ashworth Scale (MMAS)] and quality-adjusted life year (QALY) being the main indicators of effectiveness. However, both the duration of the studies and the evaluation of costs were highly heterogeneous, making comparison difficult. In conclusion, both BTX-A and DN are cost-effective to treat spasticity in patients with stroke, but there is a need for comparative studies to make direct comparisons of cost-effectiveness with the most frequently used outcomes such as the MMAS and QALYs.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".