Abstract 43: Predicting Clinical Outcomes After Thrombolysis in Patients with Diabetes in Acute Ischemic Stroke
Bibliographic record
Abstract
Background: Diabetes is a well-known stroke risk factor that is associated with poorer stroke outcomes. Limited tools are available to evaluate clinical outcomes and response to thrombolysis in stroke patients with Diabetes. Methods: We applied the iScore (www.sorcan.ca/iscore), a validated risk score, to stroke patients presenting to stroke centers participating in the Registry of the Canadian Stroke network (RCSN). Outcome measures: Favorable outcome (defined as a modified Rankin scale 0-2) at discharge after thrombolysis. Secondary outcomes included intracerebral hemorrhage, death at 30-days and at 1-year stratified by tertiles of the iScore. Results: Among 12,686 patients with an acute ischemic stroke, 3,228 (25.5%) had diabetes. Patients with diabetes had higher likelihood of death or disability (mRS>3) at discharge after thrombolysis compared to patients without diabetes (75.7% vs. 68.9%; RR 1.01 95%CI 1.02-1.18). The risk of intracranial hemorrhage (any type or symptomatic) was not different in patients with or without diabetes (12.6% vs. 12.5%; RR 1.01, 95%CI 0.72-1.4 and 7.5% vs 6.8%; RR 1.11, 95% CI 0.7-1.72 respectively). In the logistic regression analysis, there was an interaction between tPA and the iScore (p<0.001), but there was no interaction between diabetes and the iScore or tPA. Conclusion: Stroke patients with diabetes had higher mortality. The iScore similarly predicted a clinical response after tPA in both patients with and without diabetes. ICH was similar in both groups.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".