Adoption of focused ultrasound thalamotomy for essential tremor: why so much fuss about FUS?
Bibliographic record
Abstract
BACKGROUND: Focused ultrasound (FUS) was approved as a new treatment modality for essential tremor (ET) in 2016. The goal of this study was to quantify FUS adoption for ET and understand its drivers. METHODS: The adoption of the various surgical options for ET was estimated using three measures: the number of presentations on the various surgical treatments for ET at specialised international meetings, the number of original papers published as identified by literature searches and the number of thalamotomy procedures performed worldwide for ET as provided by device manufacturers' registries. RESULTS: First, we found that the number of presentations related to lesioning procedures is increasing relative to deep brain stimulation (DBS) at international meetings. Second, there are already more publications on FUS (93) than stereotactic radiosurgery (SRS) (68) or radiofrequency (43) for ET, although they still lag behind DBS papers (750). Third, the number of annual FUS thalamotomies performed for ET (n>1200 in 2019) in 44 centres has surpassed the annual procedures across 342 Gamma Knife units (n<400, 2018) but is yet to reach the number of DBS cases for ET estimated at over 2400/year. CONCLUSION: FUS is being rapidly adopted for the treatment of ET. We hypothesise that its perceived minimally invasive nature coupled with the ability to perform intraoperative clinical assessments, its immediate effects and active marketing efforts are contributing factors. As lesioning modalities for the treatment of ET are reappraised, the superior popularity of FUS over SRS appears to arise for reasons other than differences in clinical outcomes.
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 | 0.008 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".