Factors Affecting Qualification for Thrombectomy at a Comprehensive Stroke Center Following Inter Facility Transfer
Bibliographic record
Abstract
Endovascular therapy has revolutionized acute stroke management associated with large vessel occlusions (LVO). The province of Saskatchewan currently has 8 primary stroke centres (PSC) and 1 comprehensive stroke centre (CSC) spread over a large geographical area. Seventy three percent of the population lives outside the Saskatoon metropolitan area, which houses the provinceu2019s single comprehensive stroke centre. In an effort to decrease treatment disparities, the Saskatchewan acute stroke pathway worked in conjunction with all PSCu2019s to ensure best practice delivery of stroke services. Despite this, geography remains a significant barrier to providing endovascular therapy in a timely manner to eligible patients, knowing that the time to recanalization is a vital variable in clinical outcome. In a retrospective analysis of acute stroke patients with LVOs in Saskatchewan from July 2016-February 2018, 52 patients were transferred from PSCs to the CSC for possible endovascular therapy. 71% no longer qualified for thrombectomy, due to clinical improvement, recanalization seen on CTA, or progression of stroke with low Aspects score upon reassessment at the CSC. Inter-facility deterioration risk factors were assessed using transfer metrics, patient characteristics, and initial imaging findings at the PSC to understand this patient population better.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".