The prevalence of Parkinson's disease: A systematic review and meta‐analysis
Bibliographic record
Abstract
Parkinson's Disease (PD) is a common neurodegenerative disorder. We sought to synthesize studies on the prevalence of PD to obtain an overall view of how the prevalence of this disease varies by age, by sex, and by geographic location. We searched MEDLINE and EMBASE for epidemiological studies of PD from 1985 to 2010. Data were analyzed by age group, geographic location, and sex. Geographic location was stratified by the following groups: 1) Asia, 2) Africa, 3) South America, and 4) Europe/North America/Australia. Meta-regression was used to determine whether a significant difference was present between groups. Forty-seven studies were included in the analysis. Meta-analysis of the worldwide data showed a rising prevalence of PD with age (all per 100,000): 41 in 40 to 49 years; 107 in 50 to 59 years; 173 in 55 to 64 years; 428 in 60 to 69 years; 425 in 65 to 74 years; 1087 in 70 to 79 years; and 1903 in older than age 80. A significant difference was seen in prevalence by geographic location only for individuals 70 to 79 years old, with a prevalence of 1,601 in individuals from North America, Europe, and Australia, compared with 646 in individuals from Asia (P < 0.05). A significant difference in prevalence by sex was found only for individuals 50 to 59 years old, with a prevalence of 41 in females and 134 in males (P < 0.05). PD prevalence increases steadily with age. Some differences in prevalence by geographic location and sex can be detected.
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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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.031 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".