In vivo assessment of trabecular and cortical bone microstructure.
Bibliographic record
Abstract
In addition to bone mineral density (BMD), bone microstructure is a major contributor to bone strength. With recently developed technologies we are able to assess bone microstructure in vivo . These technologies include high-resolution peripheral quantitative computed tomography (HR-pQCT), multi-detector computer tomography (MDCT), and high-resolution magnetic resonance imaging (HR-MRI). Using HR-pQCT both cortical and trabecular microstructure can be assessed with a voxel size of 82μm. While MDCT and HR-MRI have lower spatial resolution than HR-pQCT, they have the main advantage of imaging central sites such as the proximal femur. Using these technologies a variety of parameters can be measured including bone volume ratio, trabecular thickness and number, cortical thickness, and cortical porosity. In vivo microstructure measurements are associated with fracture risk and these measurements can be combined with finite element modeling to estimate bone strength. While limitations exist, such as measurement of only peripheral sites and motion artifacts, the assessment of microstructure is promising and provides clinically relevant information. The techniques may help to better predict fracture risk and determine the efficacy of treatments for metabolic bone diseases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".