Abstract TP213: Predictors of Functional Independence Among Acute Ischemic Stroke Patients Treated With Intra-Arterial Thrombolysis as Adjunct to Mechanical Thrombectomy
Bibliographic record
Abstract
Background and Purpose: Although intra-arterial thrombolysis (IAT) is widely used as adjunct with mechanical thrombectomy in acute ischemic stroke patients, the patients who are likely to benefit are not known. We analyzed real world data to identify acute ischemic stroke patients who are likely to benefit with IA as adjunct to mechanical thrombectomy. Methods: Acute ischemic stroke patients who underwent IAT with tissue plasminogen activator (tPA)/urokinase as adjunct to mechanical thrombectomy in the Trevo Retriever multicenter registry were analyzed. Primary endpoint was functional independence at 90 days post mechanical thrombectomy defined by a modified Rankin scale (mRS) of 0-2. We compared the variables including age, gender, pre-procedure National Institute of Health Stroke Scale (NIHSS) score, baseline Alberta Stroke Program Early CT (ASPECT) score, location of occlusion, procedure time, Thrombolysis in Cerebral Infarction (TICI) score pre- and post IAT, and number of thrombectomy passes. Results: A total of 145 patients treated with IAT tPA/urokinase after undergoing mechanical thrombectomy were analyzed. 74 patients (51%) had an mRS of 0-2 while 71 (49%) had an mRS of 3-6 (p=0.48). The procedure time for the patients in group A was 66.52±35.44 minutes as compared to group B, which was 99.01±62.97 (p=0.004). Pre-procedure NIHSS score, NIHSS at discharge, and baseline ASPECT scores were comparably lower for group A (p<0.001). Number of passes during thrombectomy correlated significantly with 90-day mRS with a value of 2.31±1.49 in group A and 3.25±2.01 in group B (p=0.002). Clot location, previous stroke, TICI scores, and stent location did not show any statistical significance. Conclusion: We did not identify any differences in mRS in patients who were treated with IAT after mechanical thrombectomy. Minimum procedure time and lesser number of passes for thrombectomy are related with good neurological outcomes at 90 days.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".