Outcomes from stereotactic surgery for essential tremor
Bibliographic record
Abstract
There are several different surgical procedures that are used to treat essential tremor (ET), including deep brain stimulation (DBS) and thalamotomy procedures with radiofrequency (RF), radiosurgery (RS) and most recently, focused ultrasound (FUS). Choosing a surgical treatment requires a careful presentation and discussion of the benefits and drawbacks of each. We conducted a literature review to compare the attributes and make an appraisal of these various procedures. DBS was the most commonly reported treatment for ET. One-year tremor reductions ranged from 53% to 63% with unilateral Vim DBS. Similar improvements were demonstrated with RF (range, 74%-90%), RS (range, 48%-63%) and FUS thalamotomy (range, 35%-75%). Overall, bilateral Vim DBS demonstrated more improvement in tremor reduction since both upper extremities were treated (range, 66%-78%). Several studies show continued beneficial effects from DBS up to five years. Long-term follow-up data also support RF and gamma knife radiosurgical thalamotomy treatments. Quality of life measures were similarly improved among patients who received all treatments. Paraesthesias, dysarthria and ataxia were commonly reported adverse effects in all treatment modalities and were more common with bilateral DBS surgery. Many of the neurological complications were transient and resolved after surgery. DBS surgery had the added benefit of programming adjustments to minimise stimulation-related complications. Permanent neurological complications were most commonly reported for RF thalamotomy. Thalamic DBS is an effective, safe treatment with a long history. For patients who are medically unfit or reluctant to undergo DBS, several thalamic lesioning methods have parallel benefits to unilateral DBS surgery. Each of these surgical modalities has its own nuance for treatment and patient selection. These factors should be carefully considered by both neurosurgeons and patients when selecting an appropriate treatment for ET.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".