Abstract TP439: Statin Treatment and Prevalent Cerebral Microbleeds: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Introduction: Statins have been reported to increase the risk of intracererbral hemorrhage, however their effects on cerebral microbleeds (CMBs) formation is not well understood. We systematically reviewed previously published studies to pool adjusted and unadjusted estimates of the association between prevalent CMBs and current statin use. Methods: We performed a systematic search in MEDLINE and SCOPUS databases on July 28 th , 2019 to identify all cohorts from randomized clinical trials or observational studies reporting CMB prevalence and statin use. We extracted cross-sectional data on CMBs presence, as provided by each study, in association to the history of current statin use. Associations are reported as odds ratios (ORs) with corresponding 95% confidence intervals (95%CI). Random effects model was used to calculate the pooled estimates. Results: We included 7 studies (n=3671 participants): unselected general population [n=1965], ischemic stroke [n=770], hemorrhagic stroke [n=252], hypertension [n=605] or neuroimaging based studies [n=72]. Statin use was not associated with CMBs presence in either unadjusted (OR=1.15, 95%CI: 0.76-1.74) or adjusted analyses (OR=1.01, 95%CI: 0.62-1.64). Statin use was more strongly related to lobar CMB presence (OR=2.01, 95%CI: 1.48-2.72) in unadjusted analysis. The effect size of this association was consistent, but no longer statistically significant in adjusted analysis that was confined to two eligible studies (OR=2.26, 95%CI: 0.86-5.91). Except for the analysis on the unadjusted probability of CMBs presence, considerable heterogeneity was present in all other analyses (I 2 >60%). Conclusion: Our findings suggest that statin treatment is not associated with CMBs overall, but may increase the risk of lobar CMB formation. This hypothesis deserves further investigation within magnetic resonance imaging ancillary studies of randomized trials.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.027 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".