Ipsilateral and axial tremor response to focused ultrasound thalamotomy for essential tremor: clinical outcomes and probabilistic mapping
Bibliographic record
Abstract
BACKGROUND: MR-guided focused ultrasound (MRgFUS) thalamotomy has been shown to be a safe and effective treatment for essential tremor (ET). OBJECTIVE: To investigate the effects of MRgFUS in patients with ET with an emphasis on ipsilateral-hand and axial tremor subscores. METHODS: Tremor scores and adverse effects of 100 patients treated between 2012 and 2018 were assessed at 1 week, 3, 12, and 24 months. A subgroup analysis of ipsilateral-hand tremor responders (defined as patients with ≥30% improvement at any time point) and non-responders was performed. Correlations and predictive factors for improvement were analysed. Weighted probabilistic maps of improvement were generated. RESULTS: Significant improvement in axial, contralateral-hand and total tremor scores was observed at all study visits from baseline (p<0.0001). There was no significant improvement in ipsilateral subscores. A subset of patients (n=20) exhibited group-level ipsilateral-hand improvement that remained significant through all follow-ups (p<0.001). Multivariate regression analysis revealed that higher baseline scores predict better improvement in ipsilateral-hand and axial tremor. Probabilistic maps demonstrated that the lesion hotspot for axial improvement was situated more medially than that for contralateral improvement. CONCLUSION: MRgFUS significantly improved axial, contralateral-hand and total tremor scores. In a subset of patients, a consistent group-level treatment effect was observed for ipsilateral-hand tremor. While ipsilateral improvement seemed to be less directly related to lesion location, a spatial relationship between lesion location and axial and contralateral improvement was observed that proved consistent with the somatotopic organisation of the ventral intermediate nucleus. TRIAL REGISTRATION NUMBERS: NCT01932463, NCT01827904, and NCT02252380.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".