In vivo precision of three HR-pQCT-derived finite element models of the distal radius and tibia in postmenopausal women
Bibliographic record
Abstract
BACKGROUND: The distal radius is the most common osteoporotic fracture site occurring in postmenopausal women. Finite element (FE) modeling is a non-invasive mathematical technique that can estimate bone strength using inputted geometry/micro-architecture and tissue material properties from computed tomographic images. Our first objective was to define and compare in vivo precision errors for three high-resolution peripheral quantitative computed tomography (HR-pQCT, XtremeCT; Scanco) based FE models of the distal radius and tibia in postmenopausal women. Our second objective was to assess the role of scan interval, scan quality, and common region on precision errors of outcomes for each FE model. METHODS: Models included: single-tissue model (STM), cortical-trabecular dual-tissue model (DTM), and one scaled model using imaged bone mineral density (E-BMD). Using HR-pQCT, we scanned the distal radius and tibia of 34 postmenopausal women (74 ± 7 years), at two time points. Primary outcomes included: tissue stiffness, apparent modulus, average von Mises stress, and failure load. Precision errors (root-mean-squared coefficient of variation, CV%RMS) were calculated. Multivariate ANOVA was used to compare the mean of individual CV% among the 3 HR-pQCT-based FE models. Spearman correlations were used to characterize the associations between precision errors of all FE model outcomes and scan/time interval, scan quality, and common region. Significance was accepted at P < 0.05. RESULTS: At the distal radius, CV%RMS precision errors were <9 % (Range STM: 2.8-5.3 %; DTM: 2.9-5.4 %; E-BMD: 4.4-8.7 %). At the distal tibia, CV%RMS precision errors were <6 % (Range STM: 2.7-4.8 %; DTM: 2.9-3.8 %; E-BMD: 1.8-2.5 %). At the radius, Spearman correlations indicated associations between the common region and associated precision errors of the E-BMD-derived apparent modulus (ρ = -0.392; P < 0.001) and von Mises stress (ρ = -0.297; P = 0.007). CONCLUSION: Results suggest that the STM and DTM are more precise for modeling apparent modulus, average von Mises stress, and failure load at the distal radius. Precision errors were comparable for all three models at the distal tibia. Results indicate that the noted differences in precision error at the distal radius were associated with the common scan region, illustrating the importance of participant repositioning within the cast and reference line placement in the scout view during the scanning process.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".