Abstract WMP7: Intra-arterial Therapy and Post Treatment Infarct Volumes in the Revascat Randomized Controlled Trial
Bibliographic record
Abstract
Background and Purpose: To find out if the beneficial effect of endovascular treatment (EVT) on functional outcome could be explained by a reduction in post treatment infarct volume in the REVASCAT multi-center randomized controlled trial. Methods: The REVASCAT trial was a multicenter randomized open-label trial with blinded outcome evaluation. Among 206 enrolled subjects (EVT n=103; control n=103), post treatment infarct volume was measured in 204 subjects. Post treatment infarct volumes were compared by treatment assignment and recanalization status. Appropriate statistical models were used to assess relationship between baseline clinical and imaging variables, post treatment infarct volume and functional status at 90 days [modified Rankin Scale (mRS)]. Results: Median post treatment infarct volume in all subjects was 23.7 ml (IQR 9.3-78.2 ml), in the EVT arm (16.3 ml, IQR 8.3-58.5 ml) and in the control arm (38.6 ml, IQR 11.9-86.8 ml) (p<0.02). Median post treatment infarct volume in the EVT arm in recanalizers was 14.6 ml (IQR 7.8-46.2 ml) vs. 92.9 ml (IQR 14.6-233.1 ml) in the non-recanalizers (p=0.05). Median post treatment infarct volume in the control arm in recanalizers was 18.1 ml (IQR 8.4-76.7 ml) vs. 92.9 ml (IQR 14.6-233.1 ml) in the non-recanalizers (p=0.02). Post treatment infarct volume (p<0.001) and recanalization status (p<0.01) were the only independent predictors of 90 day mRS; all baseline variables lost their relevance in this model. Conclusion: Endovascular therapy in the REVASCAT trial was associated with a significant reduction in infarct volume. Recanalization status and post treatment infarct volume best predict 90-day clinical 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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".