SU‐E‐U‐03: Noncontact Ultrasound Spectroscopy of Cortical Bone Phantoms: Reproducibility
Bibliographic record
Abstract
Purpose: In this study we update our studies presented in a talk in the ultrasound symposium in 2014 on the topic of noncontact ultrasound spectroscopy applied to cortical bone phantoms in which we found we found a high correlation between the BUA parameter and the Bone Mineral densities of the phantoms that was provided a linear regression with a r^₂as high as 0.96. The purpose of our current study was to have two separate ultrasound analyzers one at CSUDH in Carson California and a second analyzer at VN and associates in Elizabethtown, Ontario, Canada, in which the two analyzers only had minor differences in the transducers, pulse generators, and other hardware and software. Our goal was to compare the BUA parameters, speed of sound(SOS), attenuation, and spectra. We also considered changes in speed of sound, and attenuation taken 4 years ago with similar measurements made recently. Methods: We used Matlab shell files to compute SOS for 2 different methods, BUA, and attenuation for 15 or 16 phantoms that ranged in BMD from 150–1700 gm/m^3. Linear regressions were calculated for sets of two parameters. Some of the phantoms were scanned using a pixelized grid. Results: The two systems behaved similarly. There seemed to be only small changes in SOS with larger changes in BUA, that might be attributed to oxidation and loss of water. Conclusion: There was a high degree of reproducibility in most of our measurements
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".