Practices, Resources and Challenges in Parkinson's Disease Management in Asia: Movement Disorders in Asia Study Group Report
Bibliographic record
Abstract
BACKGROUND: The prevalence of Parkinson's disease (PD) is increasing markedly in Asia, highlighting the urgent need to understand the current practices and challenges in delivering comprehensive PD care in this region. OBJECTIVES: We aimed to determine the resources and facilities for comprehensive management of PD in Asia focusing on regions (South East Asia, Middle East, Indian Subcontinent, East Asia, and Central Asia) and income levels (high-income countries-HIC, upper middle-income countries-UMIC, lower middle-income countries-LMIC). METHOD: A survey-based questionnaire was deployed to the MDS affiliate societies or key neurologists in 32 countries in Asia. RESULTS: Thirty countries/territories participated in the survey. HICs have better availability of and accessibility to most health care professionals. Central Asia has the lowest availability of and accessibility to health care professionals. PD nurses are least available (33.3%) and least easily accessible (6.7%). Levodopa and anticholinergics are the most available (100%), accessible (100%) and affordable (100%) antiparkinsonian medications. Device-aid therapies are more available and accessible in East Asian countries/territories, compared to other regions. Accessibility to allied health professionals is poor (43%). Genetic testing is available in 18 (60%) countries/territories, mostly in HIC (P = 0.031). Community engagement and public health awareness campaigns are available in 21 (70%) countries/territories. Brain bank is available in seven (24.1%) countries/territories, mostly in HIC. Telemedicine is utilized in 21 (70%) countries/territories. CONCLUSION: This is the first survey-based study to highlight regional and income-based disparities on infrastructures required for comprehensive PD care in Asia. Regional collaborations between HIC and MIC may address some of these disparities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".