Evaluating the prognostic impact of hypoperfusion intensity ratio in acute ischemic stroke patients undergoing early-phase endovascular thrombectomy
Bibliographic record
Abstract
This research aimed to assess the prognostic relevance of the hypoperfusion intensity ratio (HIR) concerning 90-day outcomes in patients with acute ischemic stroke (AIS) managed within the early intervention window. A retrospective review was conducted on AIS patients who received pretreatment computed tomography perfusion imaging and endovascular thrombectomy due to large vessel occlusions in the anterior circulation between January 2020 and September 2022. Clinical data, including the Alberta Stroke Program Early Computed Tomography Score (ASPECTS) from non-contrast CT, along with perfusion metrics such as ischemic core, hypoperfusion extent, core-penumbra mismatch, and HIR, were analyzed. Patients were divided into groups with favorable (modified Rankin Scale score 0-2) and unfavorable outcomes (modified Rankin Scale score 3-6). Among the 187 patients evaluated, 95 (50.8%) had favorable outcomes. Univariate analysis showed significant associations between functional outcomes and variables like age, National Institutes of Health Stroke Scale score at admission, ASPECTS, HIR, ischemic core volume, and hypoperfusion volume (P < .05). Multivariate analysis revealed that younger age (odds ratio [OR] 1.064; 95% confidence interval [CI] 1.025-1.106, P = .001), lower National Institutes of Health Stroke Scale score at admission (OR 1.116; 95% CI 1.038-1.199, P = .003), smaller ischemic core volume (OR 1.017; 95% CI 1.002-1.033, P = .029), higher ASPECTS (OR 0.800; 95% CI 0.662-0.967, P = .021), and reduced HIR (OR 1.516; 95% CI 1.230-1.869, P = .001) independently predicted favorable outcomes at 90 days. Lower HIR was independently linked to improved functional outcomes in AIS patients receiving endovascular thrombectomy within the early intervention timeframe.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".