An intraoperative accelerometry and real-time analysis tool for magnetic resonance–guided focused ultrasound thalamotomy
Bibliographic record
Abstract
Magnetic resonance-guided focused ultrasound (MRgFUS) is one of the newest surgical treatments for essential tremor (ET). During this procedure, a lesion is created within the thalamus to mitigate tremor. Targeting is done using a combination of stereotaxy, MR tractography, and sublesional heating, with tremor assessed during the procedure to gauge therapeutic effectiveness. Currently, tremor assessments are done qualitatively, but this approach requires the tremor change to be above a subjective threshold and provides no objective record of surgical tremor progression. Here, the authors present and demonstrate an MR-compatible accelerometer with custom MATLAB analysis code and graphical user interface to record, visualize, and quantify tremor in near real-time. Results can be exported and saved for future review. This method was used in 20 surgeries, with patients experiencing a 50.7% (95% CI -64.1% to -37.3%) improvement in the treated limb per the Clinical Rating Scale for Tremor. This method does not interrupt the surgery and is quantitative. As research on optimizing MRgFUS treatment for ET continues-for example, the refinement of targeting during sublesional sonications-such quantifying and recording of tremor changes will provide rapid and objective feedback.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.008 | 0.002 |
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".