Effects of β2‐Adrenergic Agonists on Risk of Parkinson's Disease in COPD: A Population‐Based Study
Bibliographic record
Abstract
Introduction Multiple studies have investigated the role of β2‐adrenoreceptor agonists on the risk of Parkinson's disease (PD). However, whether β2‐agonist use is associated with the risk of PD in patients with chronic obstructive pulmonary disease (COPD) has not been examined to date. Objectives To examine the association between use of β2‐agonist and the risk of PD in patients with COPD. Methods A case‐control study nested within a cohort of patients with COPD using the British Columbia health administrative databases from 1997 to 2015 was performed. Among a cohort of patients with COPD, all cases of PD were identified, and matched each case to up to five controls by age and calendar time. The use of β2‐agonists was assessed between the third and fourth year preceding the date of PD diagnosis, followed by additional two years of grace period (between the first and second year preceding PD incidence) to control for PD latency. The use of β2‐agonists was categorized into three levels: regular use (≥ 1 dispensation for every 6 months), irregular use (dispensation in one to three 6‐month periods), and no use. A conditional logistic regression model was used to estimate the rate ratio of PD according to β2‐agonist use, rigorously controlling for confounding variables. Results Among 242,218 COPD patients, 732 PD cases and 3660 controls were identified. Use of β2‐agonists did not significantly affect the subsequent risk of PD (vs no use, adjusted rate ratios: regular use, 1.14 [95% CI: 0.93, 1.40, p=0.21], irregular use, 1.15 [95% CI: 0.92, 1.45, p=0.22]). Results remained consistent with competing risk sensitivity analysis. Conclusion Use of β2‐agonists does not appear to affect the risk of PD in a real‐world COPD population.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".