Association of Lesion Topography with Functional Outcomes in Acute Ischemic Stroke Patients Considered for, or Receiving, Reperfusion Therapy: A Meta-Analysis
Bibliographic record
Abstract
Background: The impact of lesion topography (LT), characterised by the Alberta Stroke Programme Early CT Score (ASPECTS), on outcomes after reperfusion therapy in acute ischemic stroke (AIS) is poorly elucidated. We investigated the prognostic accuracy of ASPECTS-based LT assessment and its association with clinical outcomes in AIS patients considered for reperfusion therapy or receiving intravenous thrombolysis (IVT), endovascular thrombectomy (EVT), or none or both. Methods: Studies were identified from PubMed with additional studies added from Google Scholar. The prevalence of individual ASPECTS regions will also be determined. The association of individual ASPECTS regions with the functional outcome at 90 days will be assessed using random-effects modelling for various cut-offs, such as 6, 7 and 8. The association of continuous ASPECTS with the functional outcome at 90 days will also be undertaken. Forest plots of odds ratios (ORs) will be generated. Results: A total of 25 studies have been included in the final analysis, encompassing 11,404 patients. Pooled estimates indicate that the highest prevalence rates were in cases involving the insula and lentiform nucleus. Subgroup analysis for ASPECTS < 6 (OR 6.10; 95% CI 2.50−14.90; p < 0.0001), ASPECTS < 7 (OR 4.58; 95% CI 1.18−17.86; p < 0.0001) and ASPECTS < 8 (OR 2.26; 95% CI 1.32−3.89; p < 0.0001) revealed a significant association with poor functional outcome at 90 days. Decreasing ASPECTS significantly increased the odds of poor functional outcomes at 90 days (SMD −1.15; 95% CI −1.77−−0.52; p < 0.0001). Conclusions: Our meta-analysis demonstrates that decreasing ASPECTS is significantly associated with poor functional outcomes. Individual ASPECTS regions associated with the highest odds of poor functional outcomes were identified. Future studies on the association of LT and clinical outcomes specific to EVT are required.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.051 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".