Fields of Forel Brain Stimulation Improves Levodopa-Unresponsive Gait and Balance Disorders in Parkinson's Disease
Bibliographic record
Abstract
BACKGROUND: Gait and balance disturbance are challenging symptoms in advanced Parkinson's disease (PD). Anatomic and clinical data suggest that the fields of Forel may be a potential surgical target to treat these symptoms. OBJECTIVE: To test whether bilateral stimulation centered at the fields of Forel improves levodopa unresponsive freezing of gait (FOG), balance problems, postural instability, and falls in PD. METHODS: A total of 13 patients with levodopa-unresponsive gait disturbance (Hoehn and Yahr stage ≥3) were included. Patients were evaluated before (on-medication condition) and 1 yr after surgery (on-medication-on-stimulation condition). Motor symptoms and quality of life were assessed with the Unified Parkinson's Disease Rating scale (UPDRS III) and Quality of Life scale (PDQ-39). Clinical and instrumented analyses assessed gait, balance, postural instability, and falls. RESULTS: Surgery improved balance by 43% (95% confidence interval [CI]: 21.2-36.4 to 35.2-47.1; P = .0012), reduced FOG by 35% (95% CI: 15.1-20.3 to 8.1-15.3; P = .0021), and the monthly number of falls by 82.2% (95% CI: 2.2-6.9 to -0.2-1.7; P = .0039). Anticipatory postural adjustments, velocity to turn, and postural sway measurements also improved 1 yr after deep brain stimulation (DBS). UPDRS III motor scores were reduced by 27.2% postoperatively (95% CI: 42.6-54.3 to 30.2-40.5; P < .0001). Quality of life improved 27.5% (95% CI: 34.6-48.8 to 22.4-37.9; P = .0100). CONCLUSION: Our results suggest that DBS of the fields of Forel improved motor symptoms in PD, as well as the FOG, falls, balance, postural instability, and quality of life.
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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 | 0.000 |
| 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".