Point-of-Care Ultrasound to Detect Acute Large Vessel Occlusions in Stroke Patients: A Proof-of-Concept Study
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: A primary admission of patients with suspected acute ischemic stroke and large vessel occlusion (LVO) to centers capable of providing endovascular stroke therapy (EVT) may induce shorter time to treatment and better functional outcomes. One of the limitations in this strategy is the need for accurately identifying LVO patients in the prehospital setting. We aimed to study the feasibility and diagnostic performance of point-of-care ultrasound (POCUS) for the detection of LVO in patients with acute stroke. METHODS: We conducted a proof-of-concept study and selected 15 acute ischemic stroke patients with angiographically confirmed LVO and 15 patients without LVO. Duplex ultrasonography (DUS) of the common carotid arteries was performed, and flow profiles compatible with LVO were scored independently by one experienced and one junior neurologist. RESULTS: < 0.001). Flow profiles of the CAA allowed the detection of LVO with a sensitivity of 73%, a positive predictive value of 92 and 100%, and a c-statistics of 0.83 (95%CI = 0.65-0.94) and 0.87 (95%CI = 0.69-0.94) (experienced neurologist and junior neurologist, respectively). In comparison with clinical stroke scales, DUS was associated with better trade-off between sensitivity and specificity. CONCLUSION: POCUS in acute stroke setting is feasible, it may serve as a complementary tool for the detection of LVO and is potentially applicable in the prehospital phase.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".