Supplementary Material for: Mechanical Thrombectomy in Elderly Stroke Patients with Mild-to-Moderate Baseline Disability
Bibliographic record
Abstract
Background: The number of elderly patients suffering from ischemic stroke is rising. Randomized trials of mechanical thrombectomy (MT) generally exclude patients over the age of 80 years with baseline disability. The aim of this study was to understand the efficacy and safety of MT in elderly patients, many of whom may have baseline impairment. Methods: Between January 2015 and April 2017, 96 patients ≥80 years old who underwent MT for stroke were selected for a chart review. The data included baseline characteristics, time to treatment, the rate of revascularization, procedural complications, mortality, and 90-day good outcome defined as a modified Rankin Scale (mRS) score of 0–2 or return to baseline. Results: Of the 96 patients, 50 had mild baseline disability (mRS score 0–1) and 46 had moderate disability (mRS score 2–4). Recanalization was achieved in 84% of the patients, and the rate of symptomatic hemorrhage was 6%. At 90 days, 34% of the patients had a good outcome. There were no significant differences in good outcome between those with mild and those with moderate baseline disability (43 vs. 24%, p = 0.08), between those aged ≤85 and those aged > 85 years (40.8 vs. 26.1%, p = 0.19), and between those treated within and those treated beyond 8 h (39 vs. 20%, p = 0.1). The mortality rate was 38.5% at 90 days. The Alberta Stroke Program Early CT Score (ASPECTS) and the National Institutes of Health Stroke Scale (NIHSS) predicted good outcome regardless of baseline disability (p < 0.001 and p = 0.009, respectively). Conclusion: Advanced age, baseline disability, and delayed treatment are associated with suboptimal outcomes after MT. However, redefining good outcome to include return to baseline functioning demonstrates that one-third of this patient population benefits from MT, suggesting the real-life utility of this treatment.
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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 | 0.001 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.839 | 0.259 |
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".