Micro-ultrasound for preclinical imaging
Bibliographic record
Abstract
Over the past decade, non-invasive preclinical imaging has emerged as an important tool to facilitate biomedical discovery. Not only have the markets for these tools accelerated, but the numbers of peer-reviewed papers in which imaging end points and biomarkers have been used have grown dramatically. High frequency 'micro-ultrasound' has steadily evolved in the post-genomic era as a rapid, comparatively inexpensive imaging tool for studying normal development and models of human disease in small animals. One of the fundamental barriers to this development was the technological hurdle associated with high-frequency array transducers. Recently, new approaches have enabled the upper limits of linear and phased arrays to be pushed from about 20 to over 50 MHz enabling a broad range of new applications. The innovations leading to the new transducer technology and scanner architecture are reviewed. Applications of preclinical micro-ultrasound are explored for developmental biology, cancer, and cardiovascular disease. With respect to the future, the latest developments in high-frequency ultrasound imaging are described.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.040 | 0.024 |
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".