Improving Door-to-needle Times in the Treatment of Acute Ischemic Stroke Across a Canadian Province: Methodology
Bibliographic record
Abstract
BACKGROUND: Alteplase is a proven medical treatment for acute ischemic stroke; however, the effectiveness of this treatment is highly time dependent. Therefore, it is imperative that hospitals treat acute ischemic stroke patients as quickly as possible. The measure, door-to-needle time, is the time from hospital arrival to when alteplase administration begins. OBJECTIVE: The goal in the Canadian province of Alberta was to reduce the door-to-needle time to a median of 30 minutes and to increase the percent of patients treated within 60 minutes to 90%. OVERVIEW OF METHODOLOGY: A modified version of Institute for Healthcare Improvement Breakthrough Series Collaborative was used. All stroke centers self-enrolled into the collaborative after initial contact, and sites created interdisciplinary teams to participate in the Collaborative. Leadership and faculty were highly experienced in quality improvement and acute stroke. There were 3 daylong face-to-face learning sessions that were attended by enrolled teams, which included presentation about the evidence, site presentations to promote cross-site learning, and time to plan changes with their teams. The sites were also supported by site visits, webinars, and data feedback.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: yes | Observational | medium |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: yes | Other design | medium |
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".