Comparison of hr-pQCT & MRTA to DXA & QUS for the Ex-vivo Assessment of Bone Strength
Bibliographic record
Abstract
There is a pressing need for better assessment of bone strength as current clinical tools do not directly measure bone mechanical properties, but offer only surrogate measures of bone strength. We conducted an ex-vivo study of emu bones to examine how two investigative devices, hr-pQCT and MRTA, compare to current clinical tools (DXA and QUS) in predicting true bone mechanical properties. We found that hr-pQCT parameters were able to assess bone strength as well as DXA and better than QUS, while MRTA was able to predict bone strength well in low-density but not high-density bones. Our results suggest that both hr-pQCT, which has the unique ability to specifically assess the various determinants of bone strength, and MRTA, which measures a bone mechanical property (stiffness), have great potential for use as clinical tools that can assess various components of bone strength not measured by current devices.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".