Transcutaneous auricular vagus nerve stimulation for long-term post-stroke cognitive impairment: a DTI case report
Bibliographic record
Abstract
Purpose: Long-term post-stroke cognitive impairment (PSCI) exhibits an accelerated rate of long-term cognitive decline, which can impair communication, limit social engagement, and increase rate of institutional dependence. The aim of this case report is to provide evidence for the potential of home-based transcutaneous auricular vagus nerve stimulation (taVNS) for home-bound patients with severe, long-term PSCI. Methods: A 71-year-old male suffered a stroke two and a half years ago, which imaging reported foci of cerebral infarction visible in the left temporal and parietal lobes. The patient was performed taVNS twice a day for 30 min, 5 times a week for 8 weeks. The patient was evaluated the changes of cognitive function and brain white matter at 4 time points: baseline (t0), 4 weeks without taVNS after baseline (t1), 4 weeks of intervention (t2), and 8 weeks of intervention (t3). The effect of taVNS on white matter changes was visualized by DTI. Results: After 8 weeks of taVNS treatment, the scores of Montreal cognitive assessment improved and the time to complete the shape trails test decreased. The DTI results showed that white matter in bilateral dorsal lateral prefrontal cortex remodeled after taVNS. Conclusion: Eight-week home-based taVNS may be beneficial to long-term PSCI. Further studies of home-based taVNS treating patients with long-term PSCI are needed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".