An international multicenter retrospective study to survey the landscape of thrombectomy in the treatment of anterior circulation acute ischemic stroke: outcomes with respect to age
Bibliographic record
Abstract
BACKGROUND: Thrombectomy is an efficacious treatment for acute ischemic stroke (AIS). However, relatively few studies to date have specifically examined the impact and clinical implications of age on outcomes for thrombectomy in anterior AIS. OBJECTIVE: To provide a snapshot of patient metrics and outcomes with respect to age following thrombectomy for anterior AIS to supplement the current body of data for predictors of clinical outcomes in a real-world setting. METHODS: Data were collected for 20 consecutive patients with AIS treated with thrombectomy at 15 high-volume stroke centers across North America between 2015 and 2016. Patients with anterior occlusions were dichotomized based on whether they were older or younger than 80 years. Ordinal logistic regression analyzed how clinical variables impacted disability using 90-day modified Rankin Scale (mRS) scores. RESULTS: Adequate revascularization (TICI ≥2B) was achieved in 92.3% of patients aged <80 years with an average 1.7±0.1 passes taken with the primary technique and in 88.0% of patients aged ≥80 years with an average 1.7±0.2 passes. Despite similar baseline characteristics, mRS scores were significantly higher in older patients postoperatively and at 90 days after intervention. Age was a significant predictor of 90-day mRS across the study population. CONCLUSION: This analysis affirms age is a significant determinant of 90-day mRS scores following thrombectomy for large vessel anterior AIS. Further investigation into risks faced by elderly patients during thrombectomy may provide actionable information to help refine patient selection and improve outcomes.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".