Magnetic Resonance Imaging-Guided Focused Ultrasound Lesioning under General Anesthesia: A Case Series
Bibliographic record
Abstract
INTRODUCTION: Real-time monitoring during MR-guided focused ultrasound (MRgFUS) procedures has been considered essential to monitor tremor improvement and side effects in the alignment and/or verify phase before the actual MRgFUS treatment and following the ablative sonications. However, a subgroup of patients does not tolerate being awake during the entire procedure for a variety of reasons. CASE PRESENTATIONS: We performed MRgFUS treatments in three Parkinson's disease/Parkinsonism patients under general anesthesia. These patients had previously failed an attempt to undergo the procedure awake. All 3 patients who had the procedure under general anesthesia experienced significant improvement of their symptoms and experienced only transient adverse effects (e.g., balance problems, left facial droop) that were no longer evident at their first postoperative visit. CONCLUSION: Our findings suggest that MRgFUS treatment under general anesthesia could possibly be done safely and may represent a valid therapeutic option for patients unable to tolerate the procedure awake.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".