Impact of skull density ratio on efficacy and safety of magnetic resonance–guided focused ultrasound treatment of essential tremor
Bibliographic record
Abstract
OBJECTIVE: Skull density ratio (SDR) assesses the transparency of the skull to ultrasound. Magnetic resonance-guided focused ultrasound (MRgFUS) thalamotomy in essential tremor (ET) patients with a lower SDR may be less effective, and the risk for complications may be increased. To address these questions, the authors analyzed clinical outcomes of MRgFUS thalamotomy based on SDRs. METHODS: In 189 patients, 3 outcomes were correlated with SDRs. Efficacy was based on improvement in Clinical Rating Scale for Tremor (CRST) scores 1 year after MRgFUS. Procedural efficiency was determined by the ease of achieving a peak voxel temperature of 54°C. Safety was based on the rate of the most severe procedure-related adverse event. SDRs were categorized at thresholds of 0.45 and 0.40, selected based on published criteria. RESULTS: Of 189 patients, 53 (28%) had an SDR < 0.45 and 20 (11%) had an SDR < 0.40. There was no significant difference in improvement in CRST scores between those with an SDR ≥ 0.45 (58% ± 24%), 0.40 ≤ SDR < 0.45 (i.e., SDR ≥ 0.40 but < 0.45) (63% ± 27%), and SDR < 0.40 (49% ± 28%; p = 0.0744). Target temperature was achieved more often in those with an SDR ≥ 0.45 (p < 0.001). Rates of adverse events were lower in the groups with an SDR < 0.45 (p = 0.013), with no severe adverse events in these groups. CONCLUSIONS: MRgFUS treatment of ET can be effectively and safely performed in patients with an SDR < 0.45 and an SDR < 0.40, although the procedure is more efficient when SDR ≥ 0.45.
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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 | 0.002 | 0.009 |
| 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".