Transcranial Magnetic Stimulation Combined With Multimodality Aphasia Therapy for Chronic Poststroke Aphasia
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Intensive speech therapy may improve recovery from poststroke aphasia. Further evidence suggests that pairing repetitive transcranial magnetic stimulation (rTMS) with intensive speech therapy might augment outcomes. This sham-controlled randomized clinical trial evaluated the efficacy of 1-Hz rTMS over the right pars triangularis combined with multimodality aphasia therapy (M-MAT) in chronic poststroke aphasia. METHODS: A parallel-group, double-blind, sham-controlled randomized clinical trial was conducted between April 2021 and May 2023 at an outpatient neurorehabilitation clinic. Individuals with chronic nonfluent aphasia after left middle cerebral artery stroke (>6 months from stroke) were enrolled and randomly assigned to receive either rTMS or sham stimulation combined with 35 hours of M-MAT over 10 days. The primary outcome was the Western Aphasia Battery aphasia quotient (WAB-AQ) measured at 3 weeks and 15 weeks. Intention-to-treat analysis examined treatment effects over time using linear mixed models. RESULTS: = 0.024). DISCUSSION: Intensive administration of M-MAT alone improves speech production in patients with chronic poststroke aphasia. Combining 1-Hz rTMS with M-MAT is associated with supplemental improvements in aphasia severity at follow-up. rTMS is a promising candidate as an adjuvant therapy to M-MAT. TRIAL REGISTRATION INFORMATION: ClinicalTrials.gov Identifier: NCT04102228. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that in patients with aphasia 6 or more months after a stroke, 1-Hz rTMS combined with intensive M-MAT improves WAB-AQ more than sham stimulation plus M-MAT.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".