Abstract 146: Optimal Workflow and Process Based Performance Measures for Endovascular Therapy in Acute Ischemic Strokes From the Star Registry
Bibliographic record
Abstract
Introduction: We report on workflow and process based performance measures and its impact on clinical outcome in STAR, a recent international, multi-center, prospective, single-arm study of Solitaire FR thrombectomy in patients with large vessel anterior circulation strokes treated within 8 hrs of symptom onset. Methods: A total of 202 patients were enrolled across 14 comprehensive stroke centers in Europe, Canada and Australia. The following time intervals were measured: stroke onset to ED arrival; ED to baseline CT; CT to groin puncture; groin puncture to thrombus identification; thrombus identification to start of IA therapy and start of IA therapy to reperfusion. Effects of time of day, general anesthesia (GA) utilization and multi-modal imaging on workflow were evaluated. Patient characteristics and workflow processes associated with prolonged interval times and good clinical outcome (90-day mRS 0-2) were analyzed. Results: Distribution of all interval times are illustrated in Figure 1a. Median hospital arrival to final DSA run time was 150 mins (IQR=97 mins). Hospital arrival to final DSA run time was faster in women than men (158 vs. 139 mins). General anesthesia increased CT to groin puncture time by 22 mins and groin puncture to final DSA run by 13 minutes. Time of day or week did not effect interval times. CT based multi-modal imaging reduced time from CT to groin puncture by 24 mins. For each 60-minute increase in time from symptom onset to TICI 2b/3 (or final DSA run), a 33% decrease in odds of good clinical outcome (p<0.01) was noted independent of the effect of increased age (p<0.01), higher baseline NIHSS (p=0.02), and lower ASPECTS score (p=0.02). (Figure 1b) Conclusion: Interval times in the STAR study is a reflection of current IA therapy for patients with acute ischemic stroke. Improving workflow processes and reducing time to reperfusion could improve clinical outcomes further.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".