One-Year Healthcare Utilization for Patients That Received Endovascular Treatment Compared With Control
Bibliographic record
Abstract
Background and Purpose- Endovascular therapy has been shown to be highly efficacious based on 90-day modified Rankin Scale score. We examined actual daily healthcare utilization from stroke onset to 1 year afterward from the ESCAPE trial (Endovascular Treatment for Small Core and Anterior Circulation Proximal Occlusion With Emphasis on Minimizing CT to Recanalization Time) and registry data. Methods- We examined patients from Alberta, Canada, that was enrolled into the ESCAPE trial and the Quality Improvement and Clinical Research registry in the 2016/2017 fiscal year. Through data linkages to several administrative data sets, the daily location of each patient was assessed in various healthcare settings. Results- A total of 286 patients were analyzed, 52 patients were in the treatment arm, and 47 patients were in the control arm of the ESCAPE trial while 187 patients received endovascular therapy as usual care (2016/2017 fiscal year). The odds of a patient being out of a healthcare setting over 1 year was significantly higher when they received endovascular therapy: 3.46 (1.68-7.30) in ESCAPE trial patients and 2.00 (1.08-3.75) in the Quality Improvement And Clinical Research patients. Conclusions- Endovascular therapy significantly reduces healthcare utilization up to 1 year after a stroke.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".