Can repetitive transcranial magnetic stimulation enhance motor outcomes in cerebral infarct patients?
Bibliographic record
Abstract
The effectiveness of repetitive transcranial magnetic stimulation on the post-stroke motor recovery is not apparent. To perform an accurate evaluation, we adjusted for critical factors that determine motor outcomes, including lesion location and the state of the corticospinal tract. We only included patients with cerebral infarct in the corona radiata and with corticospinal tract interruption, apparent on diffusion tensor tractography. We retrospectively enrolled 34 patients whose diffusion tensor tractography corticospinal tract was interrupted by a cerebral infarct. The corticospinal tract state of each patient was evaluated using diffusion tensor tractography. Of the 34 patients whose corticospinal tract was interrupted on diffusion tensor tractography, 12 patients underwent repetitive transcranial magnetic stimulation treatment at the early stage after cerebral infarct (repetitive transcranial magnetic stimulation group). In comparison, 22 patients did not receive repetitive transcranial magnetic stimulation treatment (non-repetitive transcranial magnetic stimulation group). High-frequency repetitive transcranial magnetic stimulation (10 Hz) was performed on the primary motor cortex of the affected hemisphere. At the six month evaluation after the onset of the infarct, motor function was measured in each patient. In both groups, compared to their states during the initial evaluation, significant improvement was found in all measurements of motor function. However, six months after onset, no significant differences between the two groups were found in these measurement scores. When a patient's CST is interrupted, high-frequency repetitive transcranial magnetic stimulation treatment at the early stage after cerebral infarct might have no additional therapeutic effect on motor outcome. Qualified randomized controlled trials are needed to support our findings further.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".