Low‐frequency deep brain stimulation for Parkinson's disease: Great expectation or false hope?
Bibliographic record
Abstract
The long-term efficacy of subthalamic deep brain stimulation for Parkinson's disease is not always retained, and many patients lose the improvement achieved during the "second honeymoon" following surgery. Deep brain stimulation is a versatile tool, as stimulation parameters may undergo a fine-tuning depending on clinical needs. Among them, frequency is the parameter that leads to more complex scenarios because there is no generalizable relationship between its modulation and the overall clinical response, which also depends on the specific considered sign. High-frequency stimulation (>100 Hz) has shown to be effective in improving most parkinsonian signs, particularly the levodopa-responsive ones. However, its effect on axial signs (such as balance, gait, speech, or swallowing) may not be sustained, minimal, or even detrimental. For these reasons, several studies have explored the effectiveness of low-frequency stimulation (generally 60 or 80 Hz). Methods, results, and especially interpretations of these studies are quite variable. Although the use of low-frequency stimulation certainly opens new avenues in the field of deep brain stimulation, after having gathered all the available evidence in patients with subthalamic implants, our conclusion is that it might be clinically useful mainly when it lessens the detrimental effects of high-frequency stimulation. © 2016 International Parkinson and Movement Disorder Society.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".