Interfacility Transfer Directly to the Neuroangiography Suite in Acute Ischemic Stroke Patients Undergoing Thrombectomy
Bibliographic record
Abstract
Background and Purpose— In patients identified at referring facilities with acute ischemic stroke caused by a large vessel occlusion, bypassing the emergency department (ED) with direct transport to the neuroangiography suite may safely shorten reperfusion times. Methods— We conducted a single-center retrospective review of consecutive patients transferred to our facility for consideration of endovascular therapy. Patients were identified as admitted directly to the neuroangiography suite (DAN), transferred to the ED before intra-arterial therapy (ED-IA), and transferred to the ED but did not receive IA therapy (ED-IV). Results— A retrospective review of a prospectively maintained database of transfer patients between January 2013 and October 2016 with large vessel occlusions identified 108 ED-IV patients and 261 patients who underwent mechanical thrombectomy (DAN=111 patients and ED-IA=150 patients). There were no differences in baseline characteristics among the 3 groups. The median computed tomography ASPECTS (Alberta Stroke Program Early CT Score) was lower in the ED-IV group versus the ED-IA and DAN groups (8 versus 9; P =0.001). In the DAN versus ED-IA cohort, there were comparable rates of TICI2b/3 recanalization and access to recanalization time. There was significantly faster hospital arrival to groin access time in the DAN cohort (81 minutes versus 22 minutes; P =0.001). Functional independence at 90 days was comparable in the DAN versus ED-IA cohorts but worse in the ED-IV group (43% versus 44% versus 22%; P =0.001). Conclusions— DAN is safe, feasible, and associated with faster times of hospital arrival to recanalization. The clinical benefit of this approach should be assessed in a prospective randomized trial.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".