Risk of Parkinson’s disease in multiple sclerosis and neuromyelitis optica spectrum disorder: a nationwide cohort study in South Korea
Bibliographic record
Abstract
BACKGROUND: Neurodegeneration is associated with pathogenesis of both multiple sclerosis (MS) and neuromyelitis optica (NMOSD). Parkinson's disease (PD) is a representative neurodegenerative disease, however, whether MS or NMOSD is associated with risk of PD is not known. METHODS: MS and NMOSD cohorts were collected from the Korean National Health Insurance Service between 1 January 2010 and 31 December 2017, using International Classification of Diseases 10th revision diagnosis codes and information in the Rare Intractable Disease management programme. The PD incidence rate that occurred after a 1-year lag period was calculated and compared with that of a control cohort matched for age, sex, hypertension, diabetes and dyslipidaemia in a 1:5 ratio. RESULTS: The incidence rates of PD in patients with MS and NMOSD were 3.38 and 1.27 per 1000 person-years, respectively, and were higher than that of their matched control groups. The adjusted HR of PD was 7.73 (95% CI, 3.87 to 15.47) in patients with MS and 2.61 (95% CI, 1.13 to 6.02) in patients with NMOSD compared with matched controls. In both patients with MS and NMOSD, there were no significant differences in relative risk when stratified by sex, age, diabetes, hypertension and dyslipidaemia. CONCLUSION: The PD risk was higher in patients with MS and NMOSD compared with healthy controls and was particularly high in patients with MS. Further investigations should be performed to determine the pathophysiology and occurrence of PD in patients with MS and NMOSD.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".