Relative Effect of Stroke Severity and Age on Outcomes of Mechanical Thrombectomy in Acute Ischemic Stroke
Bibliographic record
Abstract
echanical thrombectomy (MT) has become the standard of care for patients presenting with anterior circulation large vessel occlusion (LVO) acute ischemic strokes.In the context of limited resources (eg, interventional neuroradiologists), substantial procedural costs, and globally increasing stroke cases, identifying patients who are more likely to benefit from MT is of crucial relevance.Stroke severity and age are readily available and strong determinants of outcomes in patients receiving MT in clinical trials, and they heavily influence the decision of whether to perform an MT. 1 The efficacy of MT in patients with severe strokes is clearly larger than among those with less severe deficits.1 In clinical trials, age is associated with worse outcomes in patients with LVO, with or without MT. 1 In observational studies, MT in the older age group (eg, ≥80 years old) is associated with lower likelihood of shift to better outcomes and higher rates of death 2 than in clinical trials, raising red flags regarding the benefit of MT in the elderly population in the real-world setting.3 Importantly, the interplay between stroke severity and age as well as the relative weight of each variable on outcomes are poorly understood.This knowledge gap has the potential to lead to suboptimal therapeutic decisions, underscoring the need for more research on this topic.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".