P.194 In search of real-world neuroprotection in mechanical thrombectomy for ischemic stroke
Bibliographic record
Abstract
Background: The promise of neuroprotection for stroke remains elusive. Common medications in endovascular stroke thrombectomy have putative neuroprotective mechanisms in basic science literature. We evaluated our stroke registry for evidence that these medications have any impact on clinically meaningful outcome. Methods: A retrospective stroke thrombectomy database was evaluated for clinical and angiographic outcomes of patients receiving IV or IA tPA, Heparin, or Verapamil during procedure. Univariate analysis evaluated associations with periprocedure hemorrhage, recanalization, and functional outcomes. Results: 284 patients underwent mechanical thrombectomy over 2.75 years. For periprocedural hemorrhage, IV tPA (OR 0.457, CI 0.261-0.811, p=0.008) and Heparin (1.897, CI 1.112-3.205, p=0.019) had significant relationships. No medication had impact on favorable recanalization (TICI 2b/3). Heparin had a negative impact on 90day mRS 0-2 (OR 0.563, CI 0.348-0.901, p=0.023). Favorable recanalization remains associated with favorable outcomes at 90days (OR 2.066, CI 1.063-4.069, p=0.0361). Conclusions: While the adjunctive use of 3 commonly used periprocedural medications have a logical role in the mechanical thrombectomy eg IA tPA for clot lysis, they do not have clinical benefit that represents neuroprotection. Multivariate analysis may show more effect. A role for intraarterial neuroprotective agents exists given only 45% of patients in this series achieved functional independence.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.054 | 0.010 |
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".