What's new in surgical treatment for dystonia?
Bibliographic record
Abstract
It is now established that pallidal deep brain stimulation (DBS) is effective in the treatment of generalized and segmental primary dystonia, although there is still insufficient evidence to support its benefit in focal and secondary dystonia. Because several studies have demonstrated that pallidal DBS improves quality of life (QoL), reduced QoL and disability that are nonresponsive to medical treatment are probably the main factors guiding the decision to consider surgery. Some studies have indicated that young patients with primary dystonia who have shorter disease duration and less severe dystonia are likely to have the best outcome from DBS. Therefore, surgery should not be delayed when disability and QoL are impaired to the extent that justifies the surgical risk. A case-by-case approach is recommended in patients who have secondary dystonia. The globus pallidus internus is considered the best target for dystonia. There are still not enough data about the effectiveness of thalamic, subthalamic nucleus, and premotor cortex stimulation. Targeting with multiple electrodes and intra-individual comparisons of outcomes may help determine which target would be more beneficial. With regard to the role of lesions, pallidotomy for dystonia is still performed in several countries and can play a role in selected patients. New technologies are already available to improve the stimulation programming for DBS patients and to increase battery longevity. In the near future, it is possible that we will be able to shape stimulation settings according to disease type and symptoms. © 2013 Movement Disorder Society.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".