Abstract TP437: Statin Treatment and Accrual of Covert Cerebral Ischemia on Neuroimaging: A Systematic Review and Meta-Analysis of Randomized Trials
Bibliographic record
Abstract
Introduction: Stroke prevention is an established benefit of statin therapy, but the effects of statin treatment on the accrual of MRI markers of ischemic cerebral injury remain unknown. Methods: We performed a systematic review of MEDLINE and SCOPUS databases from inception to July 29 th , 2019 to identify all studies that randomized patients to statin treatment and assessed the effect of statin treatment on incident infarcts (asymptomatic and symptomatic), covert infarcts (asymptomatic evident only in neuroimaging) and white matter hyperintensity (WMH) accrual on magnetic resonance imaging. We included only studies reporting WMH change following normal distribution. We used random effects model to calculate the pooled estimates of the crude risk ratios (RRs) and standardized mean differences (SMDs). Results: We included data from 3 randomized controlled trials with a total of 1399 participants evaluating the effect of rosuvastatin (10mg/d) in 637 hypertensive patients older than 60 years of age over 5 years, pravastatin (40mg/d) in 554 elderly people more than 70 years of age over 3 years and simvastatin (20mg/d) in 208 patients with asymptomatic middle cerebral artery stenosis over 2 years. Patients randomized to statin treatment had decreased accrual of new infarcts (RR=0.59; 95%CI: 0.36, 0.95), new covert infarcts (RR=0.64, 95%CI: 0.46-0.89). Only one study reported WMH decreased volume change in patients randomized to statin treatment compared to patients randomized to non-statin treatment (SMD= -1.16; -1.33, -1.00). Conclusion: Our findings suggest that statin treatment can reduce the accrual of covert MRI markers of ischemic cerebral injury. Dose-response effect and population disparities need to be investigated in future studies.
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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 | 0.021 | 0.053 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.037 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".