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Record W3005677905 · doi:10.1093/aje/kwaa012

β 2-Agonists and the Incidence of Parkinson Disease

2020· article· en· W3005677905 on OpenAlexafffund
Francesco Giorgianni, Pierre Ernst, Sophie Dell’Aniello, Samy Suissa, Christel Renoux

Bibliographic record

VenueAmerican Journal of Epidemiology · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPharmacological Effects and Assays
Canadian institutionsJewish General Hospital
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health ResearchParkinson Canada
KeywordsMedicineConfidence intervalRate ratioInternal medicineCohortAgonistIncidence (geometry)Parkinson's diseaseCohort studyLogistic regressionDisease

Abstract

fetched live from OpenAlex

A recent study found a decreased risk of Parkinson disease (PD) associated with the β2 adrenergic agonist (β2-agonist) salbutamol. However, other mechanisms might explain this apparent association. Using the UK Clinical Practice Research Datalink, we formed a cohort of 2,430,884 patients aged 50 years or older between 1995 and 2016. During follow-up, 8,604 cases of PD were identified and matched to 86,040 controls on sex, age, date of cohort entry, and duration of follow-up, after applying a 1-year latency time window. Incidence rate ratios of PD associated with use of β2-agonists were estimated using conditional logistic regression. Ever-use of β2-agonists was associated with a 17% decreased rate of PD (rate ratio = 0.83, 95% confidence interval: 0.75, 0.91) compared with no use. However, this association was limited to early short-term use and was no longer observed after more than 2 years of cumulative duration of use (rate ratio = 0.97, 95% confidence interval: 0.80, 1.17). A similar pattern was observed when stratifying by time since first β2-agonist prescription and by duration of follow-up. The apparent association of β2-agonists with a decreased risk of PD is likely the result of reverse causality rather than a biological effect of these drugs on the risk of PD.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.571
Threshold uncertainty score0.463

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.036
GPT teacher head0.300
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations22
Published2020
Admission routes2
Has abstractyes

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