Six Action Steps to Address Global Disparities in Parkinson Disease
Bibliographic record
Abstract
Importance: The Global Burden of Disease study conducted between 1990 and 2016, based on a global study of 195 countries and territories, identified Parkinson disease (PD) as the fastest growing neurological disorder when measured using death and disability. Most people affected by PD live in low- and middle-income countries (LMICs) and experience large inequalities in access to neurological care and essential medicines. This Special Communication describes 6 actions steps that are urgently needed to address global disparities in PD. Observations: The adoption by the 73rd World Health Assembly (WHA) of resolution 73.10 to develop an intersectoral global action plan on epilepsy and other neurological disorders in consultation with member states was the stimulus to coordinate efforts and leverage momentum to advance the agenda of neurological conditions, such as PD. In April 2021, the Brain Health Unit at the World Health Organization convened a multidisciplinary, sex-balanced, international consultation workshop, which identified 6 workable avenues for action within the domains of disease burden; advocacy and awareness; prevention and risk reduction; diagnosis, treatment, and care; caregiver support; and research. Conclusions and Relevance: The dramatic increase of PD cases in many world regions and the potential costs of PD-associated treatment will need to be addressed to prevent possible health service strain. Across the board, governments, multilateral agencies, donors, public health organizations, and health care professionals constitute potential stakeholders who are urged to make this a priority.
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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 | 0.057 | 0.058 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.006 | 0.027 |
| Research integrity | 0.031 | 0.039 |
| Insufficient payload (model declined to judge) | 0.023 | 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".