P.007 Web-based monitoring for cognitive decline following deep brain stimulation
Bibliographic record
Abstract
Background: There is a pressing need to monitor the cognitive outcomes of patients undergoing deep brain stimulation (DBS) for movement disorders, despite the prevalence of pre-operative cognitive evaluations. Previous research has demonstrated the potential for DBS to induce reversible cognitive decline, highlighting the need for post-operative cognitive monitoring. Methods: To address this issue, the present study sought to improve upon the existing Autonomous Cognitive Examination through the development of a 5-minute web-based exam. This examination leverages the capacity of machine-learning algorithms to evaluate complex multimodal inputs, including cognitive and movement disorders, and is made available through a web-based platform for physicians to administer to their patients. Results: The outcome of this study was the development of a cognitive evaluation platform, which enables physicians to administer and view results of a brief cognitive examination with sensitivity to multiple domains of cognition, including movement disorders. The web-application based screening examination is easily accessible and can be used on any device. Conclusions: This web-based cognitive examination offers a crucial solution for monitoring longitudinal cognition in high-risk patient populations undergoing DBS for movement disorders. Its ability to assess complex multimodal inputs has broad applications beyond movement disorders and serves as a valuable tool for detecting cognitive decline.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".