Amelioration of acquired stuttering following thalamic deep brain stimulation
Bibliographic record
Abstract
Stuttering is a speech disorder that can have debilitating effects on quality of life. We present a case report of a patient with near complete resolution of acquired stuttering following thalamic deep brain stimulation (DBS) for essential tremor. A literature review of neuromodulation for both developmental and acquired stuttering is presented with proposed insights into the pathophysiology of acquired stuttering. A case report of a patient with acquired stuttering receiving thalamic DBS for essential tremor is presented. Clinical data on their stuttering severity and its impact on quality of life was prospectively collected before and six months after thalamic DBS for their essential tremor. Additional data on tremor severity, mood, cognition and overall quality of life are presented. At six months follow-up, there were significant improvements in the patient’s tremor and overall quality of life (as expected). There was also near complete resolution of their acquired stuttering and a resultant improvement in voice-related quality of life. This case report details a patient with near complete resolution of acquired stuttering following thalamic deep brain stimulation for essential tremor. The Vim nucleus of the thalamus may play an important role in the pathophysiology of acquired stuttering. Additional studies will be needed to confirm the usefulness of thalamic DBS in acquired stuttering. • Deep brain stimulation at different targets in the brain has been shown to affect both acquired and developmental stuttering • This case report is the first to show unilateral thalamic deep brain stimulation can ameliorate acquired stuttering in a patient with essential tremor • The pathophysiology under pinning acquired stuttering may involve overlapping brain circuits with a node in the ventral intermediate nucleus of the thalamus
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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) | 0.000 | 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".