Bone guided-wave ultrasonography: How far are we from clinical implementation?
Bibliographic record
Abstract
Bone guided-wave ultrasonography uses mechanical waves to study the long cortical bones. Long cortex has soft tissues above and marrow below acting like an ultrasound waveguide. The reverberations of the longitudinal and shear waves within the waveguide interfere constructively to generate energetic ultrasonic guided waves (UGW) travelling along the cortex. The UGW can be generated and recorded using an axial transmission technique with the transmitter and receiver deployed axially along the axis of the long bone on the skin’s surface. The UGW thus acquired can be analyzed to provide information relevant to thickness and mechanical properties of the long bone. In this communication, we present an update of our research efforts on bone guided-wave ultrasonography including data acquisition, multichannel signal processing, bone modeling, and implementation of inversion algorithms to recover cortical thickness and mechanical parameters from UGW data. We also present some technical challenges, which will call for the joint effort of the bone ultrasound community to advance the science.
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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 | 0.052 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.012 | 0.023 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.014 | 0.018 |
| Insufficient payload (model declined to judge) | 0.014 | 0.011 |
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".