Improving access and efficiency of ischemic stroke treatment across four Canadian provinces using a stepped wedge trial: Methodology
Bibliographic record
Abstract
Introduction Ischemic stroke is treatable with thrombolysis and/or endovascular treatment. Both treatments are highly time dependent, as faster treatment results in better outcomes. Utilization of both of these treatments is less than optimal, and treatment times continue to exceed the recommended benchmarks. An improvement intervention was launched across Atlantic Canada, which has four provinces: Nova Scotia (NS), New Brunswick (NB), Prince Edward Island (PEI), and Newfoundland and Labrador (NL). The intervention was conducted through the ACTEAST (Atlantic Canada Together Enhancing Acute Stroke Treatment) Project, which aimed to improve access and efficiency of treatment for acute ischemic stroke patients. Intervention and methods The improvement intervention was a 6-month virtual Improvement Collaborative that consisted of each stroke center assembling an interdisciplinary team, 2 full-day Learning Sessions, five to six 1-h webinars, and a site visit for each team. The Improvement Collaborative intervention was implemented using a stepped-wedge trial design, where the intervention was delivered in 3 phases. The Improvement Collaborative was initially conducted with NS, followed by NB and PEI, and the final phase was with NL. The number of participants enrolled across all 34 hospitals were 98, 86, and 72 for NS, NB-PEI, and NL, respectively. The attendance at the Learning Sessions ranged from 43 to 81 across all 3 clusters. The attendance at webinars had a mean of 29.0 (SD 6.8), 26.0 (SD 6.3), and 19.0 (SD 8.5) for the NS, NB-PEI, and NL clusters respectively. (Anticipated) Results We anticipate that an additional 3–5% of ischemic stroke patients will receive thrombolysis, EVT, or both. Additionally, we anticipate a reduction of 10–15 min in door-to-needle times across the region. This will translate to an increase in the proportion of ischemic stroke patients that will be discharged home from acute care. Discussion High level of engagement is possible in an Improvement Collaborative Intervention when implemented using a stepped-wedge trial design. The highest level of engagement was observed in the NS cluster, which maybe because this province has the most established provincial stroke system. Physician engagement, a critical aspect of improvement, was high. COVID-19 restrictions likely led to lower attendance at site visits.
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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 | 0.048 | 0.040 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".