Computed tomography perfusion-based selection of endovascularly treated acute ischaemic stroke patients – Are there lessons to be learned from the pre-evidence era?
Bibliographic record
Abstract
Introduction Some of the latest groundbreaking trials suggest that noncontrast cranial computed tomography and computed tomography-angiography are sufficient tools for patient selection within six hours of symptom onset. Before endovascular stroke therapy became the standard of care, patient selection was one of the most useful tools to avoid futile reperfusions. We report the outcomes of endovascularly treated stroke patients selected with a perfusion-based paradigm and discuss the implications in the current era of endovascular treatment. Material and methods After an interdisciplinary meeting in September 2012 we agreed to select thrombectomy candidates primarily based on computed tomography perfusion with a cerebral blood volume Alberta Stroke Program Early Computed Tomography Scale (CBV-ASPECTS) of <7 being a strong indicator of futile reperfusion. In this study, we retrospectively screened all patients with an M1 thrombosis in our neurointerventional database between September 2012 and December 2014. Results In 39 patients with a mean age of 69 years and a median admission National Institute of Health Stroke Scale of 17 the successful reperfusion rate was 74% and the favourable outcome rate at 90 days was 56%. Compared to previously published data from our database 2007-2011, we found that a two-point increase in median CBV-ASPECTS was associated with a significant increase in favourable outcomes. Conclusion Computed tomography perfusion imaging as an additional selection criterion significantly increased the rate of favourable clinical outcome in patients treated with mechanical thrombectomy. Although computed tomography perfusion has lost impact within the six-hour period, we still use it in cases beyond six hours as a means to broaden the therapeutic window.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".