Distinguishing Air From Solid Emboli Using Ultrasound: <i>In‐Vitro</i> Study of the Effect of Doppler Carrier Frequency
Bibliographic record
Abstract
OBJECTIVE: To compare the ability of the signal relative-intensity and sample-volume-length (SVL) to discriminate air bubbles from solid spheres in an in-vitro model using two different carrier frequencies of the Doppler transducer. METHODS: A gel ultrasound phantom was connected to a circuit in which blood-mimicking fluid circulated. Air bubbles (100-140 microm) and latex spheres (125 +/- 10 microm) were injected into the circuit and interrogated using 1- and 2-MHz transducers. High-intensity-transient-signals (HITS) were recorded with a dual-gated transcranial Doppler (TCD) system. Receiver-Operating-Characteristic curves determined the best cut-off points that would distinguish between embolic materials. RESULTS: HITS from air bubbles had higher intensities and longer SVL than solid spheres with either transducer (P < .0001). Air bubbles (P < .0001) and microspheres (P= .049) showed higher intensities with the 1-MHz relative to the 2-MHz transducer. The intensity increase with the 1-MHz transducer was greater for air bubbles than microspheres (P < .0001). The discriminating efficacy of both the relative-intensity and SVL was similar between transducers (intensity, P= .201; SVL, P= .98). CONCLUSIONS: The relative-intensity and SVL are equally effective to distinguish solid from air emboli using 1- and 2-MHz transducers. Our study indicates that using a lower carrier frequency does not improve the discrimination of air from solid emboli.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".