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TRUVACE RECORD VERSION
record: TRV-2026-2474
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-11T18:01:59.995836Z
status: published
lens: g_space
sector: health
headline: Gamma knife thalamotomy for essential tremor: can machine learning predict tremor improvement on the day of treatment?
dek: Background Gamma Knife (GK) thalamotomy is a minimally invasive treatment option for medically refractory essential tremor (ET), but clinical improvement is typically delayed for several months. Machine learning may enable individualized prediction of treatment response using routinely available data at the time of treatment.Structured abstract. Methods We retrospectively analyzed 163 GK thalamotomy procedures performed in 139 patients with ET. A random forest model was developed using demographic, clinical, bas…
gain_title: In a retrospective study of 139 essential tremor patients, a machine learning model using day-of-treatment data predicted tremor improvement after Gamma Knife thalamotomy with an R² of 0.79 and an average error of 16 percentage points.
problem_title: (none)
trace_subject: (none)
gain_reading: In a retrospective study of 139 essential tremor patients, a machine learning model using day-of-treatment data predicted tremor improvement after Gamma Knife thalamotomy with an R² of 0.79 and an average error of 16 percentage points.
gain_evidence: the model achieved an R² of 0.79 and an MAE of 16 percentage points
problem_reading: (none)
problem_evidence: (none)
quick_read: Researchers built a random forest model to predict how much a patient's tremor would improve after Gamma Knife thalamotomy, using only information available before or at treatment. The retrospective analysis covered 163 procedures in 139 patients with essential tremor. On an 80/20 hold-out test the model reached an R² of 0.79 and a mean absolute error of 16 percentage points.

Improvement after Gamma Knife thalamotomy typically takes several months to appear, so patients often wait a long time to learn whether the treatment worked. The authors say a day-of-treatment forecast could support more personalized counseling and outcome forecasting in functional radiosurgery. Repeated 5-fold cross-validation gave a lower mean R² of 0.72, so the hold-out figure may be somewhat optimistic.

Predicting when benefit would begin worked much less well. The exploratory time-to-benefit model had an R² of 0.31 and an error of 1.78 months.
limitation: The model was developed retrospectively and validated only by hold-out and cross-validation, and the exploratory prediction of time to benefit was weak, with an R² of 0.31.
tag: Evidence-backed gain
key_points: The study analyzed 163 Gamma Knife thalamotomy procedures in 139 patients with medically refractory essential tremor. | The model used demographic, clinical, baseline Fahn-Tolosa-Marín (FTM) and radiosurgical planning variables available before or at treatment. | The primary outcome was percentage improvement in a four-domain composite FTM score. | Hold-out testing gave an R² of 0.79 and a mean absolute error of 16 percentage points. | Repeated 5-fold cross-validation gave a mean R² of 0.72 (95% CI, 0.64-0.79) and a mean RMSE of 20.4 percentage points. | Exploratory prediction of time to clinical benefit gave an R² of 0.31 and an MAE of 1.78 months.
rundown: The team used a random forest model trained on routinely collected data: demographics, clinical history, baseline Fahn-Tolosa-Marín tremor scores and radiosurgical planning variables. Validation combined a patient-level 80/20 hold-out test with repeated 5-fold cross-validation, and performance was measured by R², mean absolute error and root mean squared error.

Cross-validation gave a mean R² of 0.72 (95% CI, 0.64-0.79), a mean MAE of 15.8 percentage points (95% CI, 14.6-17.1) and a mean RMSE of 20.4 percentage points (95% CI, 18.7-22.2). That means predictions typically missed the true percentage improvement by about 16 points.

A separate exploratory model for time to clinical benefit was weaker, with an R² of 0.31, an MAE of 1.78 months and an RMSE of 2.34 months. The authors conclude that predicting tremor improvement on the day of treatment is feasible.
sources:
- peer_reviewed | Acta Neurologica Belgica | https://doi.org/10.1007/s13760-026-03231-5 | 2026-11-06
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