Differential Effects of Speech and Language Therapy and rTMS in Chronic Versus Subacute Post-stroke Aphasia: Results of the NORTHSTAR-CA Trial
Bibliographic record
Abstract
Background & objective Contralesional 1-Hz repetitive transcranial magnetic stimulation (rTMS) over the right pars triangularis combined with speech-language therapy (SLT) has shown positive results on the recovery of naming in subacute (5–45 days) post-stroke aphasia. NORTHSTAR-CA is an extension of the previously reported NORTHSTAR trial to chronic aphasia (>6 months post-stroke) designed to compare the effectiveness of the same rTMS protocol in both phases. Methods Sixty-seven patients with left middle cerebral artery infarcts (28 chronic, 39 subacute) were recruited (01-2014 to 07-2019) and randomized to receive rTMS (N = 34) or sham stimulation (N = 33) with SLT for 10 days. Primary outcome variables were Z-score changes in naming, semantic fluency and comprehension tests and adverse event frequency. Intention-to-treat analyses tested between-group effects at days 1 and 30 post-treatment. Chronic and subacute results were compared. Results Adverse events were rare, mild, and did not differ between groups. Language outcomes improved significantly in all groups irrespective of treatment and recovery phase. At 30-day follow-up, there was a significant interaction of stimulation and recovery phase on naming recovery ( P <.001). Naming recovery with rTMS was larger in subacute (Mdn = 1.91/IQR = .77) than chronic patients (Mdn = .15/IQR = 1.68/ P = .015). There was no significant rTMS effect in the chronic aphasia group. Conclusions The addition of rTMS to SLT led to significant supplemental gains in naming recovery in the subacute phase only. While this needs confirmation in larger studies, our results clarify neuromodulatory vs training-induced effects and indicate a possible window of opportunity for contralesional inhibitory stimulation interventions in post-stroke aphasia. NORTHSTAR trial registration https://clinicaltrials.gov/ct2/show/NCT02020421 .
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".