Statin Pretreatment and Microembolic Signals in Large Artery Atherosclerosis
Bibliographic record
Abstract
Background and Purpose— Scarce data indicate that statin pretreatment (SP) in patients with acute cerebral ischemia because of large artery atherosclerosis may be related to lower risk of recurrent stroke because of a decreased incidence of microembolic signals (MES) during transcranial Doppler monitoring. Methods— We performed a systematic review and meta-analysis of available observational studies reporting MES presence/absence or MES burden, categorized according to SP status, in patients with acute cerebral ischemia because of symptomatic (≥50%) large artery atherosclerosis. In studies with partially-published data, authors were contacted for previously unpublished information. We also performed a sensitivity analysis of studies with data on MES burden categorized according to SP status, and an additional subgroup analysis in patients receiving higher-dose SP (atorvastatin 80 mg or rosuvastatin 40 mg daily). Results— Seven eligible study protocols were identified (610 patients, 54% with SP). SP was associated with a reduced risk of MES detection during transcranial Doppler monitoring (risk ratio=0.67; 95% CI, 0.45–0.98), with substantial heterogeneity between studies ( I 2 =52%). In studies reporting MES burden (n=4), a significantly lower number of MES were identified in patients with compared with those without SP (mean difference=−0.92; 95% CI, −1.64 to –0.19), with no evidence of heterogeneity between studies ( I 2 =49%). Subgroup analysis revealed that higher-dose SP reduced the risk of detecting MES (risk ratio=0.23; 95% CI, 0.06–0.88), with no evidence of heterogeneity between studies ( I 2 =0%). Conclusions— SP seems to be associated with a lower incidence and burden of MES in patients with acute cerebral ischemia because of large artery atherosclerosis.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".