Baseline Motor Impairment Predicts Transcranial Direct Current Stimulation Combined with Physical Therapy-Induced Improvement in Individuals with Chronic Stroke
Bibliographic record
Abstract
Transcranial direct current stimulation (tDCS) can enhance the effect of conventional therapies in post-stroke neurorehabilitation. The ability to predict an individual’s potential for tDCS-induced recovery may permit rehabilitation providers to make rational decisions about who will be a good candidate for tDCS therapy. We investigated the clinical and biological characteristics which might predict tDCS plus physical therapy effects on upper limb motor recovery in chronic stroke patients. A cohort of 80 chronic stroke individuals underwent ten to fifteen sessions of tDCS plus physical therapy. The sensorimotor function of the upper limb was assessed by means of the upper extremity section of the Fugl-Meyer scale (UE-FM), before and after treatment. A backward stepwise regression was used to assess the effect of age, sex, time since stroke, brain lesion side, and basal level of motor function on UE-FM improvement after treatment. Following the intervention, UE-FM significantly improved ( p < 0.05 ), and the magnitude of the change was clinically important (mean 6.2 points, 95% CI: 5.2–7.4). The baseline level of UE-FM was the only significant predictor ( R 2 = 0.90 , F 1 , 76 = 682.80 , p < 0.001 ) of tDCS response. These findings may help to guide clinical decisions according to the profile of each patient. Future studies should investigate whether stroke severity affects the effectiveness of tDCS combined with physical therapy.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".