Developing a framework for utilizing adjunct rehabilitation therapies in motor recovery of upper extremity post stroke
Bibliographic record
Abstract
Standardization of first principles has transformed stroke rehabilitation in developed countries and helped guide the appropriate allocation of resources to ensure better outcomes for patients. There have been challenges in incorporating new evidence into stroke rehabilitation practices. The sheer number of RCTs can be daunting to the average clinician, made worse by the lack of a framework for their application. To develop a framework for the introduction of adjunct practices for the motor recovery of the upper extremity post stroke into clinical practice. A literature search following PRISMA guidelines revealed 1,307 RCTs involving rehabilitation interventions for the hemiparetic upper extremity post stroke. Therapies were divided into three categories of therapies: (1) Basic Conventional Therapy Approaches (<15% of interventions), (2) Adjunct Therapies Designed to Enhance Conventional Therapies (>85% of interventions), and (3) Treatment to Manage Complications (~9% of interventions). Adjunct Therapies, despite having a spectacular evidence base, are often not employed clinically. To encourage their clinical use, we have developed a framework that divides adjunct therapies into two categories: (1) Treatments that Stimulate the Brain (i.e. rTMS, mental practice, and virtual reality) and (2) Treatments that Peripherally Facilitate the Hemiparetic Upper Extremity (i.e. robotics, EMG Biofeedback, and Constraint-induced Movement Therapy). To allow stroke rehabilitation to continue to improve upper extremity recovery and outcomes, we propose a new intuitive framework that is based on a strong evidence base to guide clinicians and improve stroke rehabilitation.
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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 | 0.142 | 0.087 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.026 | 0.010 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.011 | 0.014 |
| Research integrity | 0.011 | 0.010 |
| 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".