Strength indices from pQCT imaging predict up to 85% of variance in bone failure properties at tibial epiphysis and diaphysis.
Bibliographic record
Abstract
Our primary objective was to validate the Bone Strength Index for compression (BSIC) by determining the amount of variance in failure load and stiffness that was explained by BSIC and bone properties at two distal sites in human cadaveric tibiae when tested in axial compression. Our secondary objective was to assess the variance in failure moment and flexural rigidity that was explained by bone properties, geometry and strength indices in the tibial diaphysis when tested in 4-point bending. Twenty cadaver tibiae pairs from 5 female and 5 male donors (mean age 74 yrs, SD 6 yrs) were measured at the distal epiphysis (4 and 10% sites of the tibial length from the distal end) and diaphysis (50 and 66% sites) by peripheral Quantitative Computed Tomography (pQCT; XCT 2000, Stratec). After imaging, we conducted axial compression tests on the distal tibia and 4-point bending tests on the diaphysis. Total bone mineral content and BSIC (product of total area and squared density of the cross-section) at the 4% site predicted 75% and 85% of the variance in the failure load and 52% and 57% in stiffness, respectively. At the diaphyseal sites 80% or more of the variance in failure moment and/or flexural rigidity was predicted by total and cortical area and content, geometry and strength indices corresponding to the axes of bending.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.002 | 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".