A Combined Power M‐mode and Single Gate Transcranial Doppler Ultrasound Microemboli Signal Criteria for Improving Emboli Detection and Reliability
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Single gate transcranial Doppler spectrogram (sgTCD) has a high variability in the detection of microembolic signals (MES), Adding Power M-mode Doppler (PMD) information may improve MES detection. Our study's aim is to derive combined PMD/sgTCD microemboli criteria to overcome this limitation. METHODS: Patients with symptomatic carotid disease were prospectively enrolled within 24 h of symptom onset underwent 1 hour TCD emboli monitoring. We reviewed disparity between PMD MES criteria and sgTCD MES criteria. We compared combined PMD/sgTCD criteria to sgTCD alone criteria by measuring the intraclass correlation coefficient (ICC). RESULTS: Of 92 patients, 28 patients had evidence of MES on sgTCD or PMD. Total MES count was 269 based on sgTCD criteria, and 326 based on combined PMD/sgTCD criteria (P= 0.005). Combined PMD/sgTCD criteria revealed 17 MESs (4.8%) based on sgTCD criteria to represent artifacts and 57 MESs (17.5%) not to be detected by sgTCD criteria. Overall ICC based on sgTCD criteria was 0.67 [95% confidence interval (CI): 0.58-0.74]; however, introducing combined PMD/sgTCD criteria resulted in a significant increase in the ICC, 0.91 (95% CI: 0.88-0.93). CONCLUSION: Our combined PMD/sgTCD criteria for MES appeared to improve the yield of MES detection. Reliability in MES detection interpretation was improved when combined PMD/sgTCD criteria was applied.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".