Statin treatment and accrual of covert cerebral ischaemia on neuroimaging: a systematic review and meta‐analysis of randomized trials
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Prevention of ischaemic stroke and cardiovascular events is an established benefit of statin therapy, but the effects of statin treatment on the accrual of magnetic resonance imaging (MRI) markers of ischaemic cerebral injury remain unknown. A systematic review was performed to identify all studies that randomized patients with cardiovascular risk factors to statin treatment and assessed the effect of statin treatment on covert infarcts (asymptomatic, evident only on neuroimaging) and white matter hyperintensity (WMH) accrual on MRI. METHODS: A systematic review in MEDLINE and Scopus from inception to 23 October 2019 was performed. A random-effects model was used to calculate the pooled estimates of the crude risk ratios and standardized mean differences. RESULTS: Data from three randomized controlled trials (1430 participants) were included evaluating the effect of rosuvastatin (10 mg/day) in 668 hypertensive patients older than 60 years of age over 5 years, pravastatin (40 mg/day) in 554 elderly people more than 70 years of age over 3 years and simvastatin (20 mg/day) in 208 patients with asymptomatic middle cerebral artery stenosis over 2 years. Patients randomized to statin treatment had decreased accrual of new covert infarcts (risk ratio 0.63, 95% confidence interval 0.46-0.88) during a mean follow-up of 2-6 years. Only one study reported WMH decreased volume change in patients randomized to statin treatment compared to patients randomized to non-statin treatment (standardized mean difference -1.17; 95% confidence interval -1.33, -1.00). CONCLUSION: Our findings suggest that, in addition to stroke prevention, statin treatment can reduce the accrual of covert MRI markers of ischaemic cerebral injury.
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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 | 0.018 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.033 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".