Abstract WP327: CT Angiogram Has High Yield in Code Stroke
Bibliographic record
Abstract
Introduction: Although studies have examined emergent CTAs of the head and neck in patients seen by neurologists who are suspected of having a large vessel occlusion, the utility and accuracy of CTA for patients called as a ‘code stroke’ by any healthcare provider has not been evaluated. Methods: At our institution, imaging for all Code Stroke patients includes non-contrast CT of the brain as well as CTA of the neck and brain, regardless of severity. This imaging is often performed prior to detailed neurology evaluation. We queried our radiology department report database for all studies labelled ‘CTA ELVO’, an imaging order code which is specific for Code Stroke. We then cross-referenced this list with our prospectively acquired ischemic stroke registry, which consists of all patients discharged with a diagnosis of acute ischemic stroke. Results: Between January and August 2017, 1265 CTA ELVOs were performed. Average age was 66.3 years, and 52.4% were female. Of all CTA ELVOs, 144 were performed on inpatients (11.3%) and neuroradiologists read 149 studies (11.8%). Critical findings on vessel imaging were present in 165 studies (13%); of these 118 patients were ultimately diagnosed with acute ischemic stroke. Studies with critical findings involved older patients (73.7 years, p<0.001) and were less likely to be performed on inpatients (29 studies, p=0.012) but were no different in number of females (55%, p=0.51) or whether read by a neuroradiologist (23 studies, p=0.36). Critical findings included acute intracranial large vessel occlusion involving the middle cerebral artery, intracranial internal carotid artery (ICA), or basilar artery (87 studies); critical cervical ICA stenosis, acute cervical ICA occlusion, or acute dissection (42 studies); M2 occlusion (32 studies); critical intracranial stenosis (7 studies) and vascular malformation such as aneurysm or arteriovenous malformation (6 studies). Conclusion: The yield of non-invasive vessel imaging in patients called as a code stroke in detecting critical findings is high, revealing abnormalities in approximately one in eight patients. Patients presenting with acute neurologic symptoms should receive vessel imaging as part of the initial workup for suspected stroke.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".