104 Examining the sex differences in the prevalence and incidence of Parkinson disease
Bibliographic record
Abstract
Background Parkinson disease (PD) is a major cause of disability affecting >6 million people worldwide. The incidence/prevalence of PD is generally higher in males than females. It is unclear whether male predominance is observed in low- and middle-income countries, where the fastest apparent rate of increase of PD has been observed. Methods We searched MEDLINE, SCOPUS and OVID for articles published between 2014-2021 for incidence of PD, 2011-2021 for prevalence, and updated previously published systematic reviews for which the last date of inclusion had been 2014 and 2011. We included 32 articles for prevalence and 30 for incidence. We calculated male/female (M/F) prevalence and incidence ratios, and investigated heterogeneity in estimates. Results The combined M/F prevalence ratio was 1.18 (95% CI 1.03-1.36) and incidence ratio was 1.37 (95% CI 1.22-1.53), lowest in Asian populations. These were not influenced by study type, national economy or mean participant age. The female-to-male gap in life expectancy did partly account for data heterogeneity. Conclusion The sex gap for prevalence of PD was smaller than has previously been reported. More studies are needed to understand the determinants of sex imbalance in PD.
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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 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".