Imaging-Based Methods for Non-invasive Assessment of Bone Properties Influenced by Mechanical Loading
Bibliographic record
Abstract
PURPOSE: To describe the most common in vivo imaging-based research tools used to assess bone properties that are influenced by mechanical loading associated with exercise, habitual physical activity, or disease states. Bone is a complex metabolically active tissue that adapts to changes in mechanical loading by altering the amount and spatial organization of mineral. METHOD: Using a narrative review design, the authors provide an overview of bone biology and biomechanics to emphasize the importance of bone size scale, porosity, and degree of mineralization when interpreting measures acquired using quantitative ultrasound (QUS), dual-energy X-ray absorptiometry (DXA), computed tomography (CT), magnetic resonance imaging (MRI), and finite element analysis (FEA). For each imaging modality, basic imaging principles, typical outcome measures associated with changes in mechanical loading, and salient features for physiotherapists are described. MAIN RESULTS: While each imaging modality has strengths and limitations, currently CT-based methods are best suited for determining the effects of mechanical loading on bone properties-particularly in the peripheral skeleton. CONCLUSIONS: Regardless of the imaging technology used, the physiotherapist must carefully consider the assumptions of the imaging-based method, the clinical context, the nature of the change in mechanical loading, and the expected time course for change in bone properties. Regardless of the imaging technology used, the physiotherapist must carefully consider the assumptions of the imaging-based method, the clinical context, the nature of the change in mechanical loading, and the expected time course for change in bone properties.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".