Evidence gaps in endovascular treatment of acute ischemic stroke
Bibliographic record
Abstract
In this PhD thesis, current evidence gaps in endovascular treatment of acute ischemic stroke are discussed. Followed by an introduction in chapter 1, we first discuss the clinical course of acute ischemic stroke due to medium vessel occlusion in chapter 2, which is not as benign as commonly assumed. In chapters 3 and 4, we assess the combined effect of baseline variables on post-stroke outcome, namely the combined effect of age and Alberta Stroke Program Early CT Score (ASPECTS) in chapter 3, and the combined effect of age and National Institutes of Health Stroke Scale (NIHSS) in chapter 4. We found no evidence of interaction between age and ASPECTS, or age and NIHSS. We further assessed the impact of different infarct patterns and tissue specific infarct volumes on 24-hour follow-up imaging after endovascular treatment in chapter 5, and found that certain infarct patterns, such as a territorial infarct pattern, are independent strong predictors of poor outcome, even when adjusting for total infarct volume. In chapter 6, we analyzed features that distinguish patients with and without fast infarct progression, and showed that cerebral vascular anatomy seems to be the most important predictor of fast infarct progression. Chapter 8 summarizes a consensus statement on antiplatelet management for carotid stenting in the setting of endovascular treatment, and chapter 9 investigates starting points for improving in-hospital acute stroke workflow efficiency. Chapter 10 provides a summary and an outlook on future directions in acute ischemic stroke research and management.
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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 | 0.017 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".