DBS-Edmonton App, a Tool to Manage Patient Expectations of DBS in Parkinson Disease
Bibliographic record
Abstract
OBJECTIVE: After deep brain stimulation (DBS) for Parkinson disease (PD), patients often do not report the level of satisfaction anticipated. This misalignment can relate to patients' expectations for an invasive treatment and insufficient knowledge of DBS's effectiveness in relieving motor and nonmotor symptoms (NMS). Patient satisfaction depends on expectations and goals for treatment. We hypothesized that improving patient education with a patient-centered shared decision-making tool emphasizing autonomy would improve patient satisfaction and clinical outcome. METHODS: We developed a computer application (DBS-Edmonton app), allowing patients with PD to input their symptoms and to learn how effective DBS addresses their prioritized symptoms. Sixty-two volunteers referred for DBS used the DBS-Edmonton app. DBS-related knowledge and patient perceptions of the DBS-Edmonton app were assessed with pre- and post-use questionnaires. Fourteen of 24 patients who proceeded to DBS achieved optimization at 6 months. Perceived functional improvement was assessed and compared with 12 control patients with DBS who did not use the DBS-Edmonton app. RESULTS: = 0.014). CONCLUSION: This interventional study showed that the DBS-Edmonton app improved DBS-related knowledge and patient satisfaction, independent of the objective motor outcome. It may assist patients in deciding to proceed to DBS and can be easily incorporated into practice to improve patient satisfaction post-DBS.
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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 | 0.007 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".