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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".