Relationships Between Densitometric and Morphological Parameters as Measured by Peripheral Computed Tomography and the Compressive Behavior of Lumbar Vertebral Bodies From Macaques (Macaca fascicularis)
Bibliographic record
Abstract
STUDY DESIGN: The measured maximum compressive force and stress of lumbar vertebral bodies from cynomolgus monkeys were compared with peripheral quantitative computed tomography (pQCT) derived densitometric and morphologic vertebral parameters. OBJECTIVES: To determine, which pQCT parameters were predictive of vertebral mechanical behavior, and of these, identify those that were the best predictors of vertebral load capacity. A secondary objective was to test the suitability of a multiple parameter-based approach for predicting vertebral mechanical load response. SUMMARY OF BACKGROUND DATA: Noninvasive methods for identifying and diagnosing changes in skeletal load tolerance are imperative for early detection of bone diseases such as osteoporosis. It is currently unclear, which densitometric and morphologic parameters are the best predictors of the mechanical performance of bone tissues. METHODS: Seventy-seven monkey lumbar vertebrae from the species Macaca fascicularis were tested. Following midbody cross-sectional pQCT scans of each specimen, specimens were loaded in axial compression until failure. The pQCT parameters were evaluated independently for correlation with the mechanical response of the vertebral bodies. The parameters were also incorporated in a stepwise linear regression analysis to determine their correlation with the mechanical behavior of the vertebrae. RESULTS: Several pQCT parameters correlated significantly with mechanical behavior. Of these, the trabecular area and the cortical/subcortical area had the strongest associations with the maximum load and stress magnitudes, respectively. The stepwise inclusion of additional predictors, while able to explain additional variance, did not significantly improve predictions of the response of the vertebral bodies to loading. CONCLUSION: pQCT parameters are quantitatively related to tissue mechanical behavior. The parameters often used in clinical evaluations were not found to be superior to some other pQCT parameters, and inferior to others. This suggests that the inclusion of additional morphologic parameters would improve estimates of the tissue mechanical tolerance over using densitometric parameters alone.
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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 | 0.000 | 0.002 |
| 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".