PREDICTORS OF OUTCOMES IN ELDERLY ACUTE STROKE PATIENTS UNDERGOING ENDOVASCULAR THROMBECTOMY
Bibliographic record
Abstract
Introduction: The aging population is one of the main reasons for the increase in stroke cases.The aim of this study was to evaluate the predictors of 3-month outcomes in patients aged ≥65 years who underwent mechanical thrombectomy for acute ischemic stroke and compare patients aged 65–79 years with those aged ≥80 years in terms of demographic characteristics, workflow, functional outcomes, and complication rates. Materials and Method: This retrospective cohort study included 169 consecutive patients aged ≥65 years who underwent mechanical thrombectomy for acute ischemic stroke due to large vessel occlusion between April 2020 and May 2023. Results: Recanalization was successful for 148 (87.57%) patients.According to multivariable logistic regression analysis results, low (≤9) Alberta Stroke Program Early Computed Tomography score (odds ratio: 4.217, 95% confidence interval: 1.209–14.715, and p=0.024), high National Institutes of Health Stroke Scale score at 24th hour (odds ratio: 1.192, 95% confidence interval: 1.087–1.306, and p<0.001), high Acute Physiology and Chronic Health Evaluation score (odds ratio: 1.127, 95% confidence interval: 1.016–1.250, and p=0.023), and intubation need (odds ratio: 15.055, 95% confidence interval: 2.087–108.612, and p=0.007) were independent predictors of poor outcome. Conclusion: The lack of significant differences in workflow, functional outcomes, and complications among patients ˃80 years of age indicates that MT is effective in this age group. Considering the aging population, identifying the predictors of 3-month outcomes after mechanical thrombectomy will help predict outcomes, better identify elderly patients who may benefit from the procedure, and guide treatment decisions. Keywords: Aged; Ischemic Stroke; Endovascular Procedures; Thrombectomy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".