P.070 Introducing Stroke Endovascular Thrombectomy Into A Smaller Canadian Site, Is It Safe?
Bibliographic record
Abstract
Background: Success of Endovascular Thrombectomy (EVT) requires ultra-fast access to specialized neuro imaging, neurological assessment and an angio suite with interventional radiologists. Prior access was via transport to Vancouver and outcomes were poor, with a high rate of disability or death. This appeared primarily due to long delays. Methods: Quality control process, in parallel to the introduction of a new intervention, EVT, to Vancouver Island, to determine if this intervention could be delivered with reasonable safety and good outcomes. Patients receiving EVT from May, 2016 until Sep, 2019 are included, with 90-day outcomes. Data was collected by stroke nurses. Results: The proportion of patients having a good outcome was comparable to that of the major clinical trial involving Canadian academic centres. The proportion sustaining a poor outcome was comparable to the control group in that trial population (who still received tPA treatment where possible). This was despite a median age 4.5 years greater than in that trial. Conclusions: EVT required coordination of multiple services. Victoria General Hospital performance in terms of speed to treatment was slower than in the published trials. This is a factor in determining outcome and is therefore an important quality improvement target moving forward.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.054 | 0.003 |
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".