Successful magnetic resonance–guided focused ultrasound treatment of tremor in patients with a skull density ratio of 0.4 or less
Bibliographic record
Abstract
OBJECTIVE: The use of magnetic resonance-guided focused ultrasound (MRgFUS) for the treatment of tremor-related disorders and other novel indications has been limited by guidelines advocating treatment of patients with a skull density ratio (SDR) above 0.45 ± 0.05 despite reports of successful outcomes in patients with a low SDR (LSDR). The authors' goal was to retrospectively analyze the sonication strategies, adverse effects, and clinical and imaging outcomes in patients with SDR ≤ 0.4 treated for tremor using MRgFUS. METHODS: Clinical outcomes and adverse effects were assessed at 3 and 12 months after MRgFUS. Outcomes and lesion location, volume, and shape characteristics (elongation and eccentricity) were compared between the SDR groups. RESULTS: A total of 102 consecutive patients were included in the analysis, of whom 39 had SDRs ≤ 0.4. No patient was excluded from treatment because of an LSDR, with the lowest being 0.22. Lesioning temperatures (> 52°C) and therapeutic ablations were achieved in all patients. There were no significant differences in clinical outcome, adverse effects, lesion location, and volume between the high SDR group and the LSDR group. SDR was significantly associated with total energy (rho = -0.459, p < 0.001), heating efficiency (rho = 0.605, p < 0.001), and peak temperature (rho = 0.222, p = 0.025). CONCLUSIONS: The authors' results show that treatment of tremor in patients with an LSDR using MRgFUS is technically possible, leading to a safe and lasting therapeutic effect. Limiting the number of sonications and adjusting the energy and duration to achieve the required temperature early during the treatment are suitable strategies in LSDR patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".