Abstract W MP6: Pre-treatment Neuroimaging Is Critical to Identify Acute Stroke Patients With Large Infarcts Unlikely to Respond to Endovascular Therapy
Bibliographic record
Abstract
Purpose: The Penumbra START Trial was a multi-center, prospective trial with an aim of testing if core infarct size on pre-treatment neuroimaging predicts clinical response to IA stroke therapy. Presented herein are final results of this study. Methods: As prespecified, infarcts were trichotomized into small [lesion volume <50 cc (CTP, DWI) or ASPECTS 8-10 (CTA)], medium (volume 50-100 cc or ASPECTS 5-7) or large (volume >100 cc or ASPECTS 0-4). In total, 146 patients were enrolled at 27 centers, including 115 patients meeting study criteria. Core infarct volumes were tiered by ranking imaging results by DWI > CTP > CTA. Results: Mean age was 66. Median NIHSS score was 19. Overall rate of TICI 2b-3 revascularization was 71% (post vs pre-procedure p <0.0001). Forty-five percent of patients had a 90-day good outcome (mRS ≤2); 27% died. Core infarct volumes were 24% small, 52% medium and 24% large; the number of patients for each imaging modality was 5% DWI, 38% CTP and 57% CTA. In tiered volume analysis, the good outcome rate was 68% in small, 46% in medium and 17% in large infarcts ( p =0.0004), despite similar recanalization rates (74% small, 73% medium, 64% large, p =0.4186). Patients with large infarcts had significantly higher admission NIHSS ( p =0.00782). Within 24 hours of intervention, the procedural SAE rate was 22% in small, 27% in medium and 46% in large infarcts ( p =0.0497). Mortality and sICH rates also showed relationships with infarct volumes (11% small, 23% medium, 50% large, p =0.0011, and 4% small, 7% medium, 21% large, p =0.0245, respectively). Conclusion: Pre-treatment neuroimaging is important to identify patients with large infarcts who are less likely to have favorable outcomes and more likely to suffer mortality, sICH and procedural SAEs. These findings support the use of stringent imaging criteria in patient selection.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".