Effect of workflow metrics on clinical outcomes of low diffusion-weighted imaging Alberta Stroke Program Early Computed Tomography Score (DWI-ASPECTS) patients subjected to mechanical thrombectomy
Bibliographic record
Abstract
BACKGROUND: Although accumulating evidence has demonstrated the benefit of mechanical thrombectomy (MT) in patients with low Alberta Stroke Program Early Computed Tomography Score (ASPECTS), it is still unclear how workflow metrics impact the clinical outcomes of this subgroup of patients. METHODS: Patients with acute stroke and diffusion-weighted imaging (DWI) ASPECTS ≤5 at baseline, who underwent MT within 6 hours of symptoms onset, were included from a prospectively maintained national multicentric registry between January 1, 2012 to August 31, 2017. The degree of disability was assessed by the modified Rankin Scale (mRS) at 90 days. The primary outcome was functional independence defined as mRS 0 to 2 at 90 days. RESULTS: The study included 291 patients with baseline DWI-ASPECTS ≤5. Good outcome was achieved in 82 (28.2%) patients, and 104 (35.7%) patients died within 90 days. Successful reperfusion (modified Thrombolysis In Cerebral Infarction (mTICI) 2b-3) rate was 75.3%, and median onset to recanalization (OTR) time was 2 268min. Among time-related variables, OTR emerged as the strongest predictor of primary outcome (adjusted OR for every 60 min 0.59, 95% CI 0.44 to 0.77; p<0.001). mTICI 2c-3 independently predicted a good outcome (adjusted OR 1.91, 95% CI 1.004 to 3.6; p=0.049) along with age and baseline DWI-ASPECTS. Recanalization status failed to significantly impact outcome in the DWI-ASPECTS 0-3 subpopulation. CONCLUSIONS: 5 treated with MT.
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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 | 0.003 | 0.013 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".