Abstract 104: Good Collateral Status and Favorable Clinical Outcome in Acute Ischemic Stroke: First Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background: Leptomeningeal collaterals preserve ischemic brain beyond an occluded artery until reperfusion is achieved. This systematic review and meta-analysis seeks to update current state of knowledge on the use of collateral imaging as a prognostic and patient selection tool in patients with acute ischemic stroke. Methods: We searched all published studies measuring collateral status in patients with acute ischemic stroke and its association with clinical outcome using MEDLINE, EMBASE and the Cochrane Systematic Review database between Jan 1951- May 2014. Imaging modality, collateral score type, time from stroke onset to evaluation and other parameters that assess study quality were collected. Association between collateral status and clinical outcome was estimated using random effects models. Meta-regression was then used to analyze heterogeneity. Results: Of 2,424 studies reviewed, 10 studies that fulfilled all inclusion/exclusion criteria, had all necessary data elements and reported adjusted point estimates were included in the final meta-analysis. Four studies used DSA to measure collaterals vs. 5 that used CTA and 1 dCTA; 6 studies included patients < 6 hours of symptom onset, 5 studies adjusted for reperfusion status while reporting point estimate while 5 studies used consensus reading. Pooled adjusted odds of good clinical outcome with good collateral status were 4.69 (95% CI 2.73-8.05) (Attached Figure 1). Only 36.3% of the variance in the pooled estimate is attributed to study heterogeneity (Q statistic p value=0.12). Meta-regression did not reveal any statistically significant difference in heterogeneity based on imaging modality (p=0.96), time from symptom onset to imaging (p=0.3), reperfusion status (p=0.82) or consensus reads (p=0.51). Begg’s test and funnel plot for small study effects suggests asymmetry (p<0.01). Conclusion: Collateral imaging is a reliable prognostic and patient selection tool in acute ischemic stroke.
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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 | 0.045 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.057 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".