Association of Leukotriene receptor antagonists (LTRAs) use and the risk of aortic stenosis
Bibliographic record
Abstract
Background: Aortic stenosis (AS) is one of the most common heart valve diseases in theWestern world.It affects about 2% people aged 65 or above and the number rises to 7% by the age of 80. Presently, only invasive surgical or expensive percutaneous aortic valve replacement are available as treatment for symptomatic patients.Thus, discovery of medical treatments that slow down the progression of aortic valve disease would be a significant therapeutic advance.In vitro studies have shown the involvement of leukotriene in the pathogenesis of AS via leukotriene receptors.Therefore, leukotriene receptor antagonists (LTRAs), an established nonsteroid asthma treatment, are a potential therapeutic target of AS.Objectives: The goal of this study was to determine whether use of LTRAs is independently associated with a lower incidence of aortic stenosis using population data from Quebec.Methods: Using the Quebec, Canada health administrative databases, a study cohort of individuals over 66, principally with suspected coronary artery disease, from April 1,2001 and March 30, 2011 was created.Individuals with previous diagnosis of AS, mitral stenosis, congenital AS were excluded from the population.The outcome was a new diagnosis of nonrheumatic AS or surgical valve replacement.A nested case-control analysis was used with controls (10:1) matched by age and follow-up duration.Exposure was defined as individuals with at least 90 cumulative days of LTRAs exposure after cohort entry.Odds ratios of AS were estimated with conditional logistic regression model with adjustment for confounders.Several sensitivity analyses, principally addressing possible misclassification bias, were performed to explore the robustness of the results. Results:The cohort consisted of 20466 cases and 204660 matched controls.Mean follow up time for cases and controls was 382 and 369 days, respectively.Compared to individuals not
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".