Predicting Clinical Outcomes and Response to Thrombolysis in Acute Stroke Patients With Diabetes
Bibliographic record
Abstract
OBJECTIVE: Few tools are available to evaluate clinical outcomes and response to thrombolysis (tPA) in stroke patients with diabetes. We explored how the iScore (www.sorcan.ca/iscore), a validated risk score, predicts clinical outcomes in stroke patients with and without diabetes. RESEARCH DESIGN AND METHODS: We applied the iScore to stroke patients presenting to stroke centers participating in the Registry of the Canadian Stroke Network. Main outcomes included favorable outcome, defined as a modified Rankin scale (mRS) 0-2 at discharge, and intracerebral hemorrhage (ICH) after tPA. RESULTS: Among 12,686 patients with an acute ischemic stroke, 3,228 (25.5%) had diabetes. Among patients receiving tPA (n = 1,689), those with diabetes had a lower rate of a favorable outcome compared with their counterparts (24.3 vs. 31.1%; RR 0.90 [95% CI 0.82-0.98]). The risk of ICH was not significantly different in patients with or without diabetes (for any type 12.6 vs. 12.5%, RR 1.01 [0.72-1.40]; for symptomatic ICH 7.5 vs. 6.8%, RR 1.11 [0.70-1.72]). The regression analysis revealed a decline in the probability of a favorable outcome after tPA with increments in the iScore (P value for iScore × tPA interaction <0.001). There was no difference in the response to tPA predicted by the iScore between stroke patients with and without diabetes (P value = 0.07). CONCLUSIONS: Stroke patients with diabetes have poorer outcomes compared with patients without diabetes, which is not explained by ICH. The iScore similarly predicts response to tPA between stroke patients with and without diabetes.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".