Advances in surgery for movement disorders
Bibliographic record
Abstract
Movement disorder surgery has evolved throughout history as our knowledge of motor circuits and ways in which to manipulate them have expanded. Today, the positive impact on patient quality of life for a growing number of movement disorders such as Parkinson's disease is now well accepted and confirmed through several decades of randomized, controlled trials. Nevertheless, residual motor symptoms after movement disorder surgery such as deep brain stimulation and lack of a definitive cure for these conditions demand that advances continue to push the boundaries of the field and maximize its therapeutic potential. Similarly, advances in related fields - wireless technology, artificial intelligence, stem cell and gene therapy, neuroimaging, nanoscience, and minimally invasive surgery - mean that movement disorder surgery stands at a crossroads to benefit from unique combinations of all these developments. In this minireview, we outline some of these developments as well as evidence supporting topics of recent discussion and controversy in our field. Moving forward, expectations remain high that these improvements will come to encompass an even broader range of patients who might benefit from this therapy and decrease the burden of disease associated with these conditions. © 2016 International Parkinson and 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".