Effects of Regular Use of ß2-Adrenergic Receptor Agonists on the Risk of Parkinson’s Disease in Patients with COPD
Bibliographic record
Abstract
Background: Patients with chronic obstructive pulmonary disease (COPD) have an elevated risk of Parkinson’s disease (PD) whereas β2-adrenoreceptor agonists, the cornerstone of COPD therapy, are associated with lower risk of PD. Objective: To examine the association between β2-agonist use and PD risk in patients with COPD. Methods: We performed a nested case-control study based on the health administrative data of British Columbia, Canada (1997–2015). Individuals with ≥40 years of age were included if they satisfied a validated case definition of COPD. Within the COPD cohort, we identified a PD sub-cohort in which patients have incurred ≥1 PD-related healthcare encounter and then filled ≥1 anti-PD medication within 90 days after the initial PD diagnosis. Each PD patient was then matched to five COPD patients without PD based on the latency period between the first COPD diagnosis and the first dispensation of anti-PD medication (≥4 years), as well as age, sex, calendar year and COPD severity. We used conditional logistic regression to estimate rate ratios to β2-agonists adjusting for potential confounder. Results: From 242,641 COPD patients, the final analysis included 662 patients in the PD sub-cohort and 3310 in the non-PD sub-cohorts (a 1:5 ratio). Therapeutic use of β2-agonists did not significantly affect the subsequent two-year risk of PD (versus no use, hazard ratios: regular use, 0.93 [95% CI: 0.76-1.16, p=0.53], irregular use, 0.96 [95% CI: 0.75–1.22, p=0.72]). Conclusion: In a population-based sample of surviving COPD patients β2-agonists users had a similar risk of developing PD as compared to the non-users.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".