<scp>MDS PD</scp> e‐Diary: A New Patient‐Centered Digital Tool in Development for People with Parkinson's Disease
Bibliographic record
Abstract
BACKGROUND: There is a need to better evaluate Parkinson's disease (PD) impact on daily lives of People with Parkinson's (PwP). OBJECTIVES: To conceptualize a novel digital tool, the MDS PD e-Diary that has been commissioned by the International Parkinson's disease and Movement Disorders Society. METHODS: Using a modified Delphi methodology, we sought consensus among key stakeholders (PwP, care partners, PD specialists, industry, regulatory representatives) through online questionnaires, focus groups, and a broad community survey. RESULTS: The consensus resulted in a multiplatform patient-reported outcome tool to track PD progression. It includes an Item Bank of symptoms and activities featuring two interconnected user modes. The personal mode is a customizable self-tracking tool that allows data sharing with professionals to improve standard care. The research mode employs a predefined responsive item to enhance research and clinical trials. CONCLUSION: The MDS PD e-Diary was designed to capture PD progression and its impact on PwP lives, potentially transforming research and clinical practice. Its further development and validation processes are underway.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".