Endovascular Thrombectomy for Acute Ischemic Stroke in Elderly Patients with Large Ischemic Cores
Bibliographic record
Abstract
Abstract Background: As the combined effects of advanced age and extensive brain infarction can have a greater negative impact on clinical outcomes, a real-world data analysis is necessary to fully understand the benefits and risks of endovascular therapy (EVT) in this population. Methods: The study retrospectively analyzed clinical outcomes for elderly stroke patients (age ≥ 70) with large ischemic cores (Alberta Stroke Program Early CT Score [ASPECTS] < 6 or ischemic cores ≥ 70 ml) in the anterior circulation using data from our prospective database between June 2018 and January 2022. The effectiveness and risks of EVT in those patients were investigated, with the primary outcome being fair outcome (modified Rankin Scale, mRS ≤ 3). Results: Among 182 elderly patients with large ischemic core volume (120 in the EVT group and 62 in the non-EVT group), 20.9% (38/182, 22.5% in the EVT group vs. 17.7% in the non-EVT group) achieved a fair outcome. Meanwhile, 49.5% (90/182, 45.8% in the EVT group vs. 56.5% in the non-EVT group) of them died at 3 months. EVT may help patients achieve functional independence. The benefits of EVT numerically exceeded non-EVT treatment for those aged ≤ ~ 85 years or with a mismatch volume ≥ ~ 50ml. However, EVT showed increased risk of symptomatic ICH after adjustment (aOR 7.279, 95%CI 1.131–46.845). Conclusion: This study highlights the significant clinical challenges faced by elderly patients with large infarction, with poor outcomes observed at 3 months. While EVT may offer some benefits, it also comes with increased risk of ICH.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".