Neurologists Preferences on Basic and Advanced Cardiac Imaging Utilization in Ischemic Stroke Patients
Bibliographic record
Abstract
INTRODUCTION: It is unknown how cardiac imaging studies are used by neurologists to investigate cardioembolic sources in ischemic stroke patients. METHODS: Between August 12, 2023, and December 8, 2023, we conducted an international survey among neurologists from Europe, North America, South America, and Asia, to investigate the frequency of utilization of cardiac imaging studies for the detection of cardioembolic sources of ischemic stroke. Questions were structured into deciles of percentage utilization of transthoracic echocardiography (TTE), transesophageal echocardiography (TEE), ECG-gated cardiac computed tomography (G-CCT), and cardiac magnetic resonance imaging (CMRI). We estimated the weighted proportion ( x¯ ) of utilization of each cardiac imaging modality, both globally and by continent. We also investigated the use of head and neck computed tomography angiography (CTA) as an emerging approach to the screening of cardioembolic sources. RESULTS: A total of 402 neurologists from 64 countries completed the survey. Globally, TTE was the most frequently used cardiac imaging technology ( x¯ = 71.2%), followed by TEE ( x¯ = 15.8%), G-CCT ( x¯ = 10.9%), and CMRI ( x¯ = 7.7%). Findings were consistent across all continents. A total of 288 respondents routinely used a CTA in the acute ischemic stroke phase (71.6%), but the CTA included a non-gated CCT in only 15 cases (5.2%). CONCLUSIONS: This survey suggests that basic cardiac imaging is not done in all ischemic stroke patients evaluated in 4 continents. We also found a substantially low utilization of advanced cardiac imaging studies. Easier to adopt screening methods for cardioembolic sources of embolism are needed.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 0.001 |
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".