Post Hoc Analyses of Randomized Clinical Trial for the Effect of Clopidogrel Added to Aspirin on Kidney Function
Bibliographic record
Abstract
Background and objectives Despite the high burden of CKD, few specific therapies are available that can halt disease progression. In animal models, clopidogrel has emerged as a potential therapy to preserve kidney function. The effect of clopidogrel on kidney function in humans has not been established. Design, setting, participants, & measurements The Secondary Prevention of Small Subcortical Strokes Study randomized participants with prior lacunar stroke to treatment with aspirin or aspirin plus clopidogrel. We compared annual eGFR decline and incidence of rapid eGFR decline (≥30% from baseline) using generalized estimating equations and interval-censored proportional hazards regression, respectively. We also stratified our analyses by baseline eGFR, systolic BP target, and time after randomization. Results At randomization, median age was 62 (interquartile range, 55–71) years old; 36% had a history of diabetes, 90% had hypertension, and the median eGFR was 81 (interquartile range, 65–94) ml/min per 1 m 2 . Persons receiving aspirin plus clopidogrel had an average annual change in kidney function of −1.39 (95% confidence interval, −1.15 to −1.62) ml/min per 1.73 m 2 per year compared with −1.52 (95% confidence interval, −1.30 to −1.74) ml/min per 1.73 m 2 per year among persons receiving aspirin only ( P =0.42). Rapid kidney function decline occurred in 21% of participants receiving clopidogrel plus aspirin compared with 22% of participants receiving aspirin plus placebo (hazard ratio, 0.94; 95% confidence interval, 0.79 to 1.10; P =0.42). Findings did not vary by baseline eGFR, time after randomization, or systolic BP target (all P values for interaction were >0.3). Conclusions We found no effect of clopidogrel added to aspirin compared with aspirin alone on kidney function decline among persons with prior lacunar stroke.
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 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 | 0.041 | 0.067 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.028 | 0.001 |
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".