Transcranial Doppler Characteristics of Different Embolic Materials During In Vivo Testing
Bibliographic record
Abstract
PURPOSE: The authors investigated whether ultrasonic characteristics of embolic signals could be used to differentiate embolic composition. MATERIALS AND METHODS: The authors analyzed high-intensity transient signals (HITS) from 3 patients with patent foramen ovale during the bubble contrast test and during total joint replacement surgery. In 3 anesthetized dogs, latex microspheres, fat particles, and air bubbles were injected into the internal carotid artery and HITS were identified in the cerebral circulation. The area under the receiver operating characteristic curve quantified the usefulness of each measure to distinguish embolic composition. RESULTS: In humans, HITS intensity (area: 0.80) and frequency (area: 0.73) but not duration (area: 0.32) were useful to distinguish air bubbles from presumed solid emboli. In animals, intensity distinguished microspheres from air (area: 0.94) and microspheres from fat (area: 0.94) but was less useful for fat and air (area: 0.64). The duration (area: 0.54-0.76) and frequency (area: 0.54-0.63) were poor discriminators. CONCLUSION: The HITS intensity best distinguished embolic composition. Particle size should be taken into account in future research.
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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 | 0.001 | 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".