Combining a tailored strength training program with transcranial direct-current stimulation (tDCS) to improve upper extremity function in chronic stroke patients
Bibliographic record
Abstract
Strengthening exercises are recommended for managing persisting weakness in the extremities post-stroke. Yet, training interventions to restore upper limb (UL) function after a stroke often produce variable outcomes because of their generic nature. For this randomized controlled trial (RCT), our primary goal was to determine whether tailoring strengthening interventions using a biomarker of corticospinal integrity, as reflected in the amplitude of motor evoked potentials (MEPs) elicited by transcranial magnetic stimulation (TMS), could lead to improved function of the affected UL. A secondary aim was to determine whether adding anodal transcranial direct current stimulation could enhance response to exercise. For this multisite RCT (Montréal, Sherbrooke, Ottawa), 80 chronic stroke adults were recruited. Pre and post training, participants underwent a clinical (Fugl-Meyer Stroke Assessment; Motor Activity Log; Range of Motion) and a TMS evaluation of their affected arm. Baseline MEPs’ amplitude served to estimate each participant’s potential for recovery: low/moderate/high. Participants were then stratified into three groups of training intensity levels, determined by the one-repetition maximum (1RM): low:35-50% 1RM/moderate:50-65% 1RM/high:70-80% 1RM. Strength training targeted the affected arm (3 times/week for 4 weeks). In each group, participants were randomly allocated into the real or sham transcranial direct current stimulation (tDCS) group (anodal montage, 2mA, 20 minutes). Results revealed significant improvements in motor function and cortical excitability in response to tailored strength training, however, no further benefits could be attributed to tDCS. Tailored strengthening program appears to be effective in improving arm function post-stroke, even at the chronic phase, but the added value of tDCS still remains equivocal. The trial is still on-going, and a total of 105 participants are expected to be recruited
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".