Improved Detection of Microbubble Signals Using Power M-Mode Doppler
Bibliographic record
Abstract
UNLABELLED: Background- Power motion-mode transcranial Doppler (TCD) (PMD) is a new, multigated technique that may simplify and enhance detection of embolus. We developed criteria for emboli detection using PMD. Then, we performed a blinded comparison of transcranial PMD with single-gate spectral TCD in TCD bubble study patients. METHODS: Patients with right-to-left shunt as detected with standard TCD were selected for this study. The international emboli criteria for spectral TCD were used. We defined novel PMD criteria for detecting emboli signature on PMD as follows: (1) signature at least 3 dB higher than the highest spontaneous PMD display of background blood flow; (2) embolic signature reflects motion in one direction at a minimum spatial extent of 7.5 mm and temporal extent of 30 ms; (3) embolus must traverse a prespecified depth. Each study was blindly assessed for microbubble signals (MBS) count on either modality. RESULTS: Thirty-six patients were included in the study. Mean age was 44.4 (SD 14.4), 50% were male, and median time from stroke onset to TCD bubble test was 12 days. Median MBS count in middle cerebral arteries (MCA) was 4 on both modalities. Spectral TCD MBS counts were highly correlated (rho=0.97) with PMD MBS counts in MCA and similarly in anterior cerebral arteries (ACA) (rho=0.79). When PMD microbubble counts in the ACA and MCA were summed, a clear 2-fold difference emerged between 2 modalities (P<0.001). CONCLUSIONS: When compared with spectral TCD, PMD detects more MBS with higher counts by identifying ACA as well as MCA emboli. Pitfalls of overcounting emboli with PMD can be avoided by following such criteria.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".