Neurological adverse event profile of magnetic resonance imaging–guided focused ultrasound thalamotomy for essential tremor
Bibliographic record
Abstract
BACKGROUND: Magnetic resonance imaging-guided focused ultrasound thalamotomy is approved by the U.S. Food and Drug Administration for treatment of essential tremor. Although this incisionless technology creates an ablative lesion, it potentially avoids serious complications of open stereotactic surgery. OBJECTIVE: To determine the safety profile of magnetic resonance imaging-guided focused ultrasound unilateral thalamotomy for essential tremor, including frequency, and severity of adverse events, including serious adverse events. METHODS: Analysis of safety data for magnetic resonance imaging-guided focused ultrasound thalamotomy (186 patients, five studies). RESULTS: Procedure-related serious adverse events were very infrequent (1.6%), without intracerebral hemorrhages or infections. Adverse events were usually transient and were commonly rated as mild (79%) and rarely severe (1%). As previously reported, abnormalities in sensation and balance were the commonest thalamotomy-related adverse events. CONCLUSION: The overall safety profile of magnetic resonance imaging-guided focused ultrasound thalamotomy supports its role as a new option for patients with medically refractory essential tremor. © 2018 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".