Neuromodulation Using Transcranial Magnetic Stimulation (TMS)
Bibliographic record
Abstract
Introduction: TMS is said to be an effective technique for motor and cognitive rehabilitation for acquired neurological lesions. This study aims to evaluate the effect of TMS in the cognition of patients after stroke. Methods: This prospective, longitudinal and interventional study was approved by the Ethics Committee (Protocol No. 54977216.3.0000.5078) and included 16 stroke victims aged from 24 to 74 years. The Montreal Cognitive Assessment (MoCA) test was used before and after the stimulation sessions and TMS was administered according to treatment protocols for a motor goal, with inhibitory (1 hz) TMS stimulation over the right and left primary motor cortex; and according to protocol for the prefrontal cortex involved in humor processing, with stimulation (10 hz) of the left dorsolateral prefrontal cortex, and inhibitory (1 hz) stimulation of the right dorsolateral prefrontal cortex. The patients underwent fifteen treatment sessions, on average. Results: Memory subtests showed improvement, and average and standard deviation values for the pre- and post-intervention periods were [2.06 (1.6) and 3.5 (1.5)], respectively. In terms of total performance, MoCA results were [18.7(3.4) and 21.1(4.03)]. Student’s t test indicated p=0.006 for performance differences in memory and p=0.003 for total performance. Conclusion: TMS was shown to be effective in achieving cognitive rehabilitation after strokes, most notably in terms of the recovery of mnemonic functions
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".