Imaging and neuromodulation in Parkinson's disease
Bibliographic record
Abstract
PURPOSE OF REVIEW: Imaging plays a key role in neuromodulation for Parkinson's disease, particularly for deep brain stimulation (DBS), which is the most frequently employed neuromodulatory treatment. Its role is rapidly expanding due to improving neuroradiological techniques. RECENT FINDINGS: Imaging is crucial at each stage of DBS care: pre, intra-, and postoperative, with roles now going beyond the traditional surgical planning and lead localization. Imaging opens the door to patient selection informed by their unique preoperative features and individualized electrode placement due to the direct visualization of targets. Imaging also permits intra-operative localization of electrodes with widely accessible fluoroscopy and offers the possibility of visualizing the orientation of segmented contacts. Advanced imaging techniques have defined anatomical sweets spots and efficacious connectomes associated with best outcomes after DBS. They also offer opportunities to develop new biomarker of successful stimulation, which is critical to the future of DBS programming. SUMMARY: Imaging should be thought as a powerful tool to push the neuromodulation field towards new boundaries focusing on personalized electrode implantation and stimulation titration. This will improve patient outcomes and inform alternative neuromodulation modalities, for which the data remain limited.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".