Abstract TP317: Hyperglycemia Strongly Predicts Large Ischemic Infarct Growth Despite Full Endovascular Reperfusion
Bibliographic record
Abstract
Introduction: Ischemic infarct core grows at variable rates despite early reperfusion. The purpose of this study was to determine the predictors of infarct growth despite full recanalization of a large vessel occlusion in acute ischemic stroke. Method: Patients with acute ischemic stroke due to ICA or MCA occlusion who received endovascular therapy with Thrombolysis in Cerebral Infarction scale (TICI) scores of 2b or greater were subsequently selected between July 2012 and May 2016. The Alberta Stroke Program Early CT Score (ASPECT) was measured on the initial CT or MRI upon arrival and subsequently on the 24-hour scan. The infarct growth (delta d) was measured as initial ASPECT minus 24-hour ASPECT. Large and small infarct growth was defined as delta d of >= to3 and < 3 respectively. The relationship between the infarct growth and baseline variables of blood glucose level(BG), time of symptoms onset to recanalization time and baseline ASPECT score were assessed using statistical analysis. Results: Total of 76 patients were included. 32% had large infarct growth (25/76). The initial ASPECT score was not significantly different between the the 2 subgroups of large and small delta d (7.5 vs 6, P= 0.97). Baseline BG level was significantly higher in the group with larger infarct growth (160 vs 128, P=0.006). The baseline BG level of more than 150 was found as the threshold between the 2 subgroups (P=0.0003). No association was found between the infarct growth and history of diabetes (P= 0.7). Conclusion: Our data suggests that infarct growth occurs in relatively high percentage of ischemic stroke patients despite early full reperfusion of the large vessel occlusion. We showed that baseline blood glucose level particularly levels of higher than 150 is significantly associated with larger infarct growth. Therefore, it can be used as a strong predictive value in early recognition of this patient population.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".