Guided wave velocity sensitivity to bone mechanical property evolution in cortical-marrow and cortical-trabecular phantoms
Bibliographic record
Abstract
Guided waves methods are sensitive to the mechanical properties of the material into which they are propagating. Guided waves methods are promising to detect osteoporosis; they are cost effective and do not expose the patient to radiation. Numerous studies have focused on the first arriving signal using a plate approximation. The aim of this study is to identify the best frequency/mode combinations for cortical-marrow and cortical-trabecular cylinder phantoms based on the sensitivity of the mode to mechanical property changes. Two different setups were used, axial transmission in a small diameter cortical-marrow phantom and circumferential propagation in a larger diameter cortical-trabecular phantom. Experiments for each method were carried out with in-plane and out-of-plane excitation, using frequencies from 50 to 200 kHz and were in good agreement with a 3D finite element model and dispersion curves extracted using semi-analytical finite element modeling. The modes’ velocity was identified using either first zero crossing, short time Fourier transform or 2D Fourier transform. The evolution of the mechanical properties from a healthy bone to an osteoporotic bone was simulated using the finite element models. The results of this study will be used to further develop the technique using highly sensitive mode and frequency combinations.
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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 | 0.003 |
| 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".