Characteristics of Pain During MRI-Guided Focused Ultrasound Thalamotomy
Bibliographic record
Abstract
BACKGROUND: Magnetic resonance imaging-guided focused ultrasound (MRgFUS) has become popular as an incisionless mode of neurosurgical treatment. However, head pain during sonication is common and its pathophysiology remains poorly understood. OBJECTIVE: To explore the characteristics of head pain occurring during MRgFUS thalamotomy. METHODS: Our study comprised 59 patients who answered questions about the pain they experienced during unilateral MRgFUS thalamotomy. The location and features of pain were investigated using a questionnaire including the numerical rating scale (NRS) to estimate maximum pain intensity and the Japanese version of the Short Form of McGill Pain Questionnaire 2 to evaluate the quantitative and qualitative dimensions of pain. Several clinical factors were investigated for possible correlation with pain intensity. RESULTS: Forty-eight patients (81%) reported sonication-related head pain, and the degree of pain was severe (NRS score ≥ 7) in 39 patients (66%). The distribution of sonication-related pain was "localized" in 29 (49%) and "diffuse" in 16 (27%); the most frequent location was the "occipital" region. The pain features most frequently reported were those in the "affective" subscale of the Short Form of McGill Pain Questionnaire 2. Patients with diffuse pain had a higher NRS score and lower skull density ratio than did patients with localized pain. The NRS score negatively correlated with tremor improvement at 6 months post-treatment. CONCLUSION: Most patients in our cohort experienced pain during MRgFUS. The distribution and intensity of pain varied according to the skull density ratio, indicating that the pain might have had different origins. Our results may contribute to the improvement of pain management during MRgFUS.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.001 | 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".