Predicting cognition after subthalamic Deep Brain Stimulation in Parkinson’s Disease
Bibliographic record
Abstract
Cognitive deficits have a high impact on quality of life in Parkinson's disease (PD). This study takes into account the multifaceted etiology of cognition to estimate the cognitive outcome after deep brain stimulation (DBS) surgery in PD. Clinical, neuropsychological, perioperative, neuroimaging- and laboratory-based risk factors for cognitive dysfunction were prospectively assessed prior to surgery in 57 patients (21 female; age 60.2 ± 8.2; disease duration 10.5 ± 5.9 years, preregistered 9 June 2019 at clinicaltrials.gov, NCT03982953). Elastic net regularized regression and leave-one-out cross-validation were used to fit a multivariable model with the Montréal Cognitive Assessment (MoCA) change one year after surgery as primary outcome. The backward span had the most robust association with the cognitive outcome (rho = 0.499, p < 0.001**; c = 0.302). We propose a post-hoc prediction model for cognition based on the baseline MoCA and backward span (R² = 0.50). After clinical validation, our short and easily applicable prediction model could improve informed therapeutic 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".