Comparison of the Burdens and Attitudes Between Standard and Web-Based Remote Programming for Deep Brain Stimulation in Parkinson Disease: Survey Study
Bibliographic record
Abstract
Background: Remote programming enables physicians to adjust implantable pulse generators over the internet for patients with Parkinson disease who have undergone deep brain stimulation (DBS) surgery. Despite these technological advances, the demand for and attitudes toward remote programming compared with standard programming among patients with Parkinson disease are still not well understood. Objective: This study aims to investigate the preferences and perceptions associated with these 2 programming methods among patients with Parkinson disease through a web-based survey. Methods: A web-based survey was administered to 463 patients with Parkinson disease who have undergone DBS surgery. The survey aimed to assess the burdens associated with postoperative programming and to compare patients' attitudes toward the 2 different programming methods. Results: A total of 225 patients completed the survey, all of whom had undergone standard programming, while 132 patients had also experienced remote programming. Among those who received standard programming, 191 (85%) patients required the support of more than 1 caregiver, 129 (58%) patients experienced over 2 days of lost work time, 98 (42%) patients incurred expenses ranging from US $42 to US $146, and 14 (6%) patients spent over US $421. Of the 132 patients who had used remote programming, 81 (62%) patients indicated a preference for remote programming in the future. However, challenges with remote programming persisted, including difficulties in obtaining official prescriptions, a lack of medical insurance coverage, and limited medical resources. Conclusions: Postoperative programming of DBS imposes significant burdens on patients and their caregivers during standard programming sessions-burdens that could be mitigated through remote programming. While patient satisfaction with remote programming is high, it is imperative for clinicians to develop personalized programming strategies tailored to the needs of different patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".