The impact of leptomeningeal collaterals in acute ischemic stroke
Bibliographic record
Abstract
Abstract Objectives: Leptomeningeal collaterals provide an alternate pathway to maintain cerebral blood flow in stroke to prevent ischemia, but their role in predicting outcome is still unclear. So, our study aims at assessing the significance of collateral blood flow (CBF) in acute stroke. Methods: Electronic databases were searched under different MeSH terms from Jan 2000 to Feb 2019. Studies were included if there was available data on good and poor CBF in acute ischemic stroke (AIS). The clinical outcomes included were modified rankin scale (mRS), recanalization, mortality, and symptomatic intracranial hemorrhage (sICH) at 90 days. Data was analyzed using random-effect model. Results: A total of 47 studies with 8,194 patients were included. Pooled meta-analysis revealed that there exist 2-fold higher likelihood of favorable clinical outcome (mRS≤2) at 90 days with good CBF compared with poor CBF (RR: 2.27; 95%CI: 1.94-2.65; p<0.00001) irrespective of the thrombolytic therapy [RR with IVT: 2.90; 95%CI: 2.14-3.94; p<0.00001, and RR with IAT/EVT: 1.99; 95% CI: 1.55-2.55; p<0.00001]. Moreover, there exists 1-fold higher probability of successful recanalization with good CBF (RR: 1.31; 95% CI: 1.15-1.49; p<0.00001). However, there was 54% and 64% lower risk of sICH and mortality respectively in patients with good CBF in AIS (p<0.00001). Conclusions: The relative risk of favorable clinical outcome is more in patients with good pretreatment CBF. This could be explained due to better chances of recanalization, combined with lesser risk of intracerebral hemorrhage in good CBF status.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.001 | 0.012 |
| 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 teacher head, 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".