Reperfusion Is a Stronger Predictor of Good Clinical Outcome than Recanalization in Ischemic Stroke
Bibliographic record
Abstract
PURPOSE: To assess the predictive value of reperfusion indices, recanalization, and important baseline clinical and radiologic scores for good clinical outcome prediction. MATERIALS AND METHODS: The study was approved by the local research ethics board. Written consent was obtained from all participants or their caregivers. Baseline computed tomography (CT) perfusion less than 4.5 hours after stroke symptoms, follow-up CT perfusion at 24 hours or less, and 5-7-day magnetic resonance images were obtained for 114 patients. Baseline imaging was assessed blinded to outcome. Recanalization status was determined at follow-up CT angiography. Reperfusion index was calculated on baseline and on follow-up at-risk tissue volume. Kruskal-Wallis, Mann-Whitney rank sum, and Spearman correlation were used for group comparisons and correlation studies. Univariate and multivariate logistic regression tested the association of clinical and imaging parameters with good outcome. Models with and without recanalization and reperfusion were compared by using Akaike information criterion. RESULTS: Reperfusion indices were significantly higher in patients with recanalization than in those without (P < .001). Despite significance of recanalization at univariate analysis, only reperfusion, age, and National Institutes of Health Stroke Scale score were significant after multivariate analysis (P < .01). Time to maximum reperfusion index had the highest accuracy (area under the receiver operating characteristic curve, 0.70) for good outcome, and reperfusion was defined as time to maximum volume of 59% or greater. Patients with reperfusion but no recanalization had significantly lower total infarct volume (P = .001) and infarct growth (P = .004) and had higher salvaged penumbra (P = .009) volumes than patients without reperfusion and recanalization. A final model with reperfusion but not recanalization was the most prognostic model of good clinical outcome. CONCLUSION: Reperfusion showed stronger association with good clinical outcome than did recanalization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".