Deep brain stimulation: Preoperative issues
Bibliographic record
Abstract
Numerous factors need to be taken into account in deciding whether a patient with Parkinson's disease (PD) is a candidate for deep brain stimulation. Patient-related personal factors including age and the presence of other comorbid disorders need to be considered. Neuropsychological and neuropsychiatric concerns relate both to the presurgical status of the patient and to the potential for surgery to result in new problems postoperatively. A number of factors related to the underlying PD need to be considered, including the specific parkinsonian motor indications (e.g., tremor, bradykinesia, gait dysfunction), previous medical therapies, including benefit from current therapy and adverse effects, and past surgical treatments. Definable causes of Parkinsonism, particularly atypical Parkinsonisms, should be considered. Finally, methods of evaluating outcomes should be defined and formalized. This is a report from the Consensus on Deep Brain Stimulation for Parkinson's Disease, a project commissioned by the Congress of Neurological Surgeons and the Movement Disorder Society (MDS). The report has been endorsed by the Scientific Issues Committee of the MDS and the American Society of Stereotactic and Functional Neurosurgery. It outlines answers to a series of questions developed to address all aspects of deep brain stimulation preoperative decision-making.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".