Reader Response: Deep Brain Stimulation in Early-Stage Parkinson Disease: Five-Year Outcomes
Bibliographic record
Abstract
We have followed with great interest the pilot trial on subthalamic nucleus deep brain stimulation (DBS) for early-stage Parkinson disease (PD). The 5-year outcomes of this trial were just published by Hacker et al.1 The authors argue that DBS reduces the risk of disease progression based on a reduced rest tremor when these patients are compared with patients who were randomized to only optimized drug therapy (ODT). Barring rest tremor, this follow-up study did not show significant difference between the ODT and DBS + ODT groups for on-therapy motor scores and measures of disability and quality of life. Although tremor can be bothersome to patients with early-stage PD, bradykinesia is clearly the disabling feature.2 Interestingly, the DBS + ODT group performance in the timed tests at 5 years1 and off-therapy bradykinesia at 2 years3 were worse, although not significant. Besides, progressive neurodegeneration may lead to gradual abatement of tremor, and tremor-dominant PD often transforms to akinetic-rigid PD.4 We certainly agree that early DBS can reduce the need of medications and possibly abolish the risk of developing dyskinesias or hallucinations in the long term. Nonetheless, we would recommend caution when stating a “Class II evidence that DBS implanted in early-stage Parkinson disease decreases the risk of disease progression”. Although similar between-group adverse event profiles were reported, 2 of the 15 DBS + ODT patients had potentially disabling adverse events: a perioperative stroke and an intracranial infection.5 In our opinion, with several variables to factor in, it is premature—and possibly dangerous—to conclude that DBS in early-stage PD reduces the risk of disease progression.
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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.016 | 0.010 |
| Insufficient payload (model declined to judge) | 0.034 | 0.024 |
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".