Abstract 168: Outcomes of Intra-Arterial Tissue Plasminogen Activator Rescue Therapy During Stroke Thrombectomy-Insights From the STAR Collaboration
Bibliographic record
Abstract
Introduction: Intra-arterial tissue plasminogen activator (IA-tPA) can be used as rescue therapy during mechanical thrombectomy for stroke patients, mostly in the setting of distal occlusion. The outcomes of IA-tPA has not been assessed in large-scale multi-center studies yet. Methods: We used data from the Stroke Thrombectomy and Aneurysm Registry (STAR), which included prospectively maintained databases of 11 thrombectomy-capable stroke centers in the US, Europe, and Asia. We compared the baseline characteristics, procedural metrics, rate of symptomatic intracranial hemorrhage (sICH), and long-term functional outcomes between thrombectomy patients who received rescue IA-tPA and a control group of thrombectomy patients with matched age, National Institute of Health stroke scale (NIHSS) on presentation, location of occlusion and IV-tPA receipt. Results: A total of 2827 thrombectomy patients were included in the STAR registry. Out of those, 205 patients received IA-tPA. We matched 191 patients from the IA-tPA group with a control group of 191 patients (table 1). No difference was seen in age, sex, race, vascular risk factors, or Alberta Stroke Program Early CT (ASPECT) score between both groups. In addition, procedural metrics, including onset to groin time, the procedure duration, and rate of successful recanalization (modified Thrombolysis in Cerebral Infarction score≥2b) were similar. Finally, similar outcomes were noted in both groups, including the rate of sICH and good 90-day functional outcome (modified Rankin scale≤2). Conclusion: The use of IA-tPA as an adjunctive treatment to mechanical thrombectomy was safe but did not result in a higher rate of successful recanalization or good long-term functional outcomes.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".