LONG-TERM ASPIRIN USE IS ASSOCIATED WITH A LOWER PREVALENCE OF FRAILTY IN MEN: THE PHYSICIANS’ HEALTH STUDY
Bibliographic record
Abstract
Chronic inflammation may lead to frailty, however the potential for anti-inflammatory medications such as aspirin to prevent frailty is unknown. We examined the association between frequency of long-term aspirin use and prevalent frailty. We conducted a cross-sectional cohort of 12,101 participants ≥60 years in the Physicians’ Health Study I, a completed aspirin randomized controlled trial (1982–1986). Annual follow-up questionnaires were sent to collect self-reported data on aspirin use, lifestyle and clinical variables. Average frequency of aspirin use was summed into 3 categories: <60 days/yr, 60–180 days/yr, and >180 days/yr. Frailty was assessed using a 33-item index administered in 1999. A score ≥0.21 was considered frail as prior studies suggest. Propensity scoring was used for statistical control of covariate influences. Logistic regression models estimated odds of prevalent frailty as a function of average aspirin use. Median age was 70 years (range 60–101). Aspirin use was reported as <60 days/yr for 15%, 61% reported 60–180 days/yr and 24% reported >180 days/yr. 2422 participants (20%) were frail. Frequency of aspirin use was positively associated with prior smoking, daily alcohol consumption, weekly exercise, hypertension, CVD and stroke, but negatively associated with prior bleeding and Coumadin use. After adjustment, the ORs (95% CIs) of prevalent frailty were 0.87 (0.77–0.99) and 0.92 (0.80–1.06) for average aspirin use 60–180 and >180 days/yr, respectively, compared to aspirin use of <60 days/yr. Long term frequency of aspirin use may be inversely associated with the prevalence of frailty among older men even after consideration of multimorbidity and health behaviors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".