Riding the Plane Wave: Considerations for In Vivo Study Designs Employing High Frame Rate Ultrasound
Bibliographic record
Abstract
Advancements in diagnostic ultrasound have allowed for a rapid expansion of the quantity and quality of non-invasive information that clinical researchers can acquire from cardiovascular physiology. The recent emergence of high frame rate ultrasound (HiFRUS) is the next step in the quantification of complex blood flow behavior, offering angle-independent, high temporal resolution data in normal physiology and clinical cases. While there are various HiFRUS methods that have been tested and validated in simulations and in complex flow phantoms, there is a need to expand the field into more rigorous in vivo testing for clinical relevance. In this tutorial, we briefly outline the major advances in HiFRUS, and discuss practical considerations of participant preparation, experimental design, and human measurement, while also providing an example of how these frameworks can be immediately applied to in vivo research questions. The considerations put forward in this paper aim to set a realistic framework for research labs which use HiFRUS to commence the collection of human data for basic science, as well as for preliminary clinical research questions.
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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 | 0.100 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".