Relative Energy Index of Microembolic Signal Can Predict Malignant Microemboli
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Microembolic signals (MES) found on transcranial Doppler range from harmless air bubbles to large, solid, particulate emboli from the heart and large vessels. The presence of MES is not always associated with poor clinical outcome. The purpose of our study was to determine whether the relative energy index of MES measured by power M-mode Doppler can distinguish malignant from benign MES and to identify patients with worse prognosis. METHODS: We prospectively collected transcranial Doppler emboli monitoring data from patients with symptomatic carotid stenosis presenting with TIA or ischemic stroke. For each patient, we calculated the relative energy index of MES and categorized those >1.0 as malignant MES. We compared the clinical characteristics, number, and volume of diffusion-weighted imaging lesions, and degree of stenosis and plaque characteristics on CT angiogram of patients with malignant and benign MES. RESULTS: We enrolled 92 patients, 29 with TIA and 63 with stroke, within 48 hours of symptom onset. Twenty-six patients had a total of 319 MES; of these, 82.4% were benign and were 17.6% malignant. Malignant MES traveled further within intracranial vessels than benign MES. The 9 patients with >1 malignant MES had significantly larger baseline diffusion-weighted imaging lesion volume, had a higher prevalence of intraluminal thrombus on CT angiogram of the neck and plaque ulceration, and were more likely to have a poor clinical outcome (modified Rankin Score > or = 2) than those with benign MES (OR, 6.5; 95% CI, 1.47-28.68). The presence of malignant MES led to the institution of more aggressive secondary prevention measures. CONCLUSIONS: Power M-mode transcranial Doppler identifies a subgroup of patients with malignant MES. These signals are more frequent in longer arterial trajectory. Patients with malignant MES have larger baseline infarcts, a higher prevalence of intraluminal thrombus or ulcerated plaque in the carotid artery, and worse clinical outcome.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".