Door to Puncture in Large Vessel Occlusions Pre‐ and Postimplementation of an Automated Image Interpretation and Communication Platform: A Single Center Study
Bibliographic record
Abstract
Background Artificial intelligence platforms, like Viz.ai with large vessel occlusion detection, have been used for disease detection and interprovider communication. Whether this software expedites patient transfer and evaluation for treatment needs further exploration. Methods A single‐center retrospective registry was queried for patients with acute large vessel occlusion of the intracranial internal carotid, middle cerebral M1 or M2 segments, or basilar artery treated in a comprehensive stroke network (8 spokes, 1 hub) for 6 months pre‐ and post‐implementation of the Viz large vessel occlusion platform (excluding a 1‐month “washout” period). Robust regression was used to summarize time from initial hospital contact to arterial puncture (primary outcome) between periods, with prespecified subgroup analyses, which were assessed using interaction terms. Results Of the 132 patients (n = 58 preintervention), there were nonsignificantly fewer patients undergoing endovascular therapy in the postintervention period (86.2% preintervention versus 73.0% postintervention; P = 0.07). Among patients who underwent endovascular therapy (n = 50 preintervention, n = 54 postintervention), there was a nonsignificant reduction in time from first contact to arterial puncture (median 155 minute preintervention versus 116 minute postintervention; P = 0.10); however, this became significant in adjusted robust regression accounting for stroke severity, age, Alberta Stroke Program Early Computed Tomography Scale score, daytime versus nighttime and weekend versus weekday arrival, and use of perfusion imaging (β −20.9 [95% CI, −40.5 to −1.4)]. There was also a significant interaction observed for the association between spoke versus hub arrival and the Viz large vessel occlusion period, with shorter intervals observed for transferred patients (n = 32 preintervention with a median of 169 versus 142 minutes for n = 33 postintervention; P interaction <0.01). Conclusion Implementation of the artificial intelligence platform was not associated with shorter intervals between initial hospital contact and neurointervention among all‐comers. A meaningful difference in time to treatment was observed among transferred patients. Larger data sets are needed to validate these observations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".