Neuropsychologic assessment of patients for movement disorder surgery
Bibliographic record
Abstract
The neuropsychologic evaluation of patients under consideration for movement disorder surgery is recognized as being an essential component of the preoperative process. Patients with early-stage concomitant dementia must be identified and the relative risk of postoperative cognitive decline evaluated. Knowledge of the patterns of an individual's strengths and weaknesses might also be a factor in deciding on a neurosurgical procedure. Although the advent of pallidal deep brain stimulation (DBS) has possibly resulted in reduced risk of induced cognitive impairment, even this procedure has been associated with negative sequelae. DBS within the subthalamic nucleus is becoming the method of choice and this may lead to cognitive and behavioral compromise, especially in the elderly patient. The team considering the establishment of neurosurgical treatment is often at a loss to decide how much neuropsychologic testing is required to determine relative risks of cognitive or behavioral morbidity as a consequence of the procedure. A brief summary of expected outcome and of pertinent family process and psychodynamic issues are addressed. This article is intended to serve as a guide to permit clinicians to choose the appropriate length and depth of neuropsychologic assessment, but also to highlight the confounding factors often present in these patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".