Multimodal therapy and use of adjunctive therapies to BoNT-A in spasticity management: defining terminology to help enhance spasticity treatment
Bibliographic record
Abstract
Spasticity management should be provided within the context of a comprehensive person-centered rehabilitation program. Furthermore, active goal setting for specific spasticity interventions is also important, with a well-established "more is better" approach. It is critical to consider adjunctive therapy and multimodal approaches if patients are not attaining their treatment goals. Often used interchangeably, there may be confusion between the terms adjunctive and multimodal therapy. Yet it is imperative to understand the differences between these approaches to achieve treatment goals in spasticity management. Addition of a secondary pharmacologic or non-pharmacologic treatment to optimize the efficacy of the initial modality, such as adding electrical stimulation or casting to BoNT-A, is considered an adjunctive therapy. Adjunctive therapy is time-specific and requires the added therapy be initiated within a specific period to enhance the primary treatment; usually within 2 weeks. Multimodal therapy is an integrated, patient-centric program of pharmacologic and non-pharmacologic strategies utilized in a concurrent/integrated or sequential manner to enhance the overall treatment effect across a variety of spasticity-associated impairments (e.g., neural and non-neural components). Moreover, within a multimodal approach, adjunctive therapy can be used to help enhance the treatment effect of one specific modality. The objectives of this paper are to clarify the differences between adjunctive and multimodal therapies, provide a brief evidence-based review of such approaches, and highlight clinical insights on selecting multimodal and adjunctive therapies in spasticity management.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".