Abstract TP39: Comparative Effectiveness of Endovascular Treatment for Acute Ischemic Stroke: A Population-Based Analysis
Bibliographic record
Abstract
Background: A heterogeneous patient population receives endovascular treatment (EVT) for acute ischemic stroke due to proximal large vessel occlusion every day. We aimed to conduct a population-based study of EVT in the province of Alberta, Canada, to understand the effectiveness in a complete population and how the magnitude of effect differs from the artificial world of clinical trials. Methods and Results: Within a three year period (April 2015 - March 2018), 576 patients fit the inclusion criteria of our study and constituted the EVT group of our analysis. The medical treatment group of the ESCAPE trial had 150 patients. Thus our total sample size was 726. We captured outcomes in clinical routine using administrative data and a linked database methodology. Primary outcome of our study was home-time. Home-time refers to the number of days that the patient was back at pre-morbid living situation without increase in level of care within 90 days of index stroke event. Median age of patients was 70 years (interquartile range (IQR) 59 - 81) and 47.8% were female. Median National Institutes of Health Stroke Scale (NIHSS) score was 17 (IQR 13 - 20). EVT was associated with an increased 90-day home-time by an average of 8.5 days compared to medical treatment alone using Cragg hurdle regression (p = 0.009). Age and higher NIHSS score were associated with decreased 90-day home-time (both p = 0.001). Multivariable logistic regression showed no association between EVT and mortality at 90 days (odds ratio 0.76, 95% confidence interval 0.47 - 1.24). Conclusions: EVT for acute ischemic stroke due to proximal large vessel occlusion was effective in our province-wide population-based study and results in increased 90-day home-time by ~8.5 days. Home-time is a novel and patient-centered outcome that reflects health circumstances that are easy to understand and meaningful to patients and their caregivers.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".