Abstract WMP15: Flat Panel Detector CT Assessment in Stroke to Reduce Times to Intra-Arterial Treatment (FAST-IA): A Study of Multi-Phase CTA in the Angiography Suite to Bypass Conventional Imaging
Bibliographic record
Abstract
Background: Bypassing the emergency department (ED) and the CT suite by directly transporting to the neuroangiography suite for imaging assessment and treatment may shorten reperfusion times while maintaining proper patient selection. Methods: Single-center prospective study of consecutive patients with anterior circulation LVO strokes transferred to our facility for consideration of endovascular therapy (ET) from 5/2016 to 12/2017. Those with basilar strokes and/or presenting to the ED were excluded. Patients were categorized into two groups: (1) F lat-Panel Detector CT A ssessment in S troke to Reduce T imes to I ntra- A rterial Treatment (FAST-IA) group, with patients transferred directly to the suite for Flat-Panel Detector multiphase CT angiography (FD-mCTA); and (2) Patients undergoing standard protocol including CT+/-CTA/CTP. The groups were matched for age, baseline NIHSS and pre-treatment glucose. Baseline characteristics, time metrics and outcomes were compared. Results: Out of 419 patients that underwent ET over the study period, 210 patients fit inclusion criteria, with 54 (25.7%) in the FAST-IA group. After matching, 49 FAST-IA/Control pairs were generated and analyzed. Baseline characteristics were well-balanced. FAST-IA patients had significantly shorter median door-to-puncture (33[26.5-47] vs 55[44.5-66] minutes, p<0.001), door-to-reperfusion (85[57.5-115.5]vs110[80-153],p=0.005) and picture-to-puncture (18[13.5-22.5]vs 42[32-47.5]minutes, p<0.001)times. There were no differences in rates of successful reperfusion (mTICI 2b-3, 95.9% vs 100%, p=0.5), parenchymal hematomas type-2 (4.1% vs 2%, p=1.00), good outcome (90-day mRS-0-2, 44.9% vs 40.8%, p=0.68) and 90-day mortality (14.3% vs 22.4%,p=0.30). Conclusions: Directly transferring patients to angiography and using FD-mCTA to determine eligibility for ET is safe and results in significant reduction in treatment times. Future larger studies are warranted.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".