Abstract WP55: Good Collateral Status Predicts Better Outcomes in Endovascular Treatment of Acute Ischemic Stroke: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Introduction: Endovascular therapy (EVT) for the treatment of acute ischemic stroke in cervical and/or cerebral arterial occlusions is superior to standard medical therapy alone. This however requires careful patient selection and the current criteria utilising time windows and the Alberta Stroke Program Early Computed Tomography Score (ASPECTS) is imperfect, limiting its efficacy. We explore the impact of pretreatment collateral status (CS) in predicting EVT outcomes. Methods: A systematic literature search was conducted on PubMed and EMBASE for randomized controlled trials and prospective and retrospective cohort studies without language restriction from January 01, 2000 to June 25, 2019. We included studies reporting efficacy and safety outcomes dichotomised by collateral status in patients with acute anterior circulation ischemic stroke that were treated with mechanical thrombectomy and/or intra-arterial thrombolysis. Odds ratios were pooled for good versus poor collaterals for outcomes based on a random-effects model. Results: The search strategy yielded fifty-four (54) studies (n=7,599) (mean age 67.6 years; females 47.4%) for quantitative analysis, of which there were 2 pairs of studies with overlapping populations but had reported different outcomes. Thus, there were at least 7,441 unique individuals included in this analysis recruited between May 1992 and September 2018. Analysis showed that good CS was strongly associated with favourable functional outcomes (modified Rankin Scale 0-2) at discharge, as well as at 3 months or follow-up (Table 1). Good CS was also associated with higher revascularization rates, lower rates of mortality and lower rates of symptomatic intracranial hemorrhage. Conclusions: Good pretreatment CS strongly predicts good functional outcome and lower complication rates. Pretreatment CS should be considered in the design of future clinical trials and as a selection criteria for EVT.
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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 | 0.014 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.029 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".