Changing Gears – <scp>DBS</scp> For Dopaminergic Desensitization in Parkinson's Disease?
Bibliographic record
Abstract
In Parkinson's disease, both motor and neuropsychiatric complications unfold as a consequence of both incremental striatal dopaminergic denervation and intensifying long-term dopaminergic treatment. Together, this leads to 'dopaminergic sensitization' steadily increasing motor and behavioral responses to dopaminergic medication that result in the detrimental sequalae of long-term dopaminergic treatment. We review the clinical presentations of 'dopaminergic sensitization', including rebound off and dyskinesia in the motor domain, and neuropsychiatric fluctuations and behavioral addictions with impulse control disorders and dopamine dysregulation syndrome in the neuropsychiatric domain. We summarize state-of-the-art deep brain stimulation, and show that STN-DBS allows dopaminergic medication to be tapered, thus supporting dopaminergic desensitization. In this framework, we develop our integrated debatable viewpoint of "changing gears", that is we suggest rethinking earlier use of subthalamic nucleus deep brain stimulation, when the first clinical signs of dopaminergic motor or neuropsychiatric complications emerge over the steadily progressive disease course. In this sense, subthalamic deep brain stimulation may help reduce longitudinal motor and neuropsychiatric symptom expression - importantly, not by neuroprotection but by supporting dopaminergic desensitization through postoperative medication reduction. Therefore, we suggest considering STN-DBS early enough before patients encounter potentially irreversible psychosocial consequences of dopaminergic complications, but importantly not before a patient shows first clinical signs of dopaminergic complications. We propose to consider neuropsychiatric dopaminergic complications as a new inclusion criterion in addition to established motor criteria, but this concept will require validation in future clinical trials. ANN NEUROL 2021;90:699-710.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".