Improvements in radiographic and clinical assessment of distal radius fracture healing by FE-estimated bone stiffness
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Bibliographic record
Abstract
Bone strength determined from finite element (FE) modelling provides an estimate of fracture healing progression following a distal radius fracture (DRF), but how these measures relate to patient-reported outcomes and functional outcomes remains unknown. We hypothesized that changes in bone stiffness and bone mineral density measured using high-resolution peripheral quantitative computed tomography (HR-pQCT) are associated with clinically available measures of functional and patient-reported outcomes. We also aimed to identify which clinical outcome measures best predict fracture stiffness and could therefore be used to inform cast removal. Participants (n = 30) with stable distal radius fractures were followed for two week intervals from the time of fracture until two months post-fracture, then at three months and six months post-fracture. At each follow-up, participants underwent clinical, radiographic, and functional assessments, as well as had their fractured wrist scanned using HR-pQCT. Recovery of bone stiffness during fracture healing was determined from micro-FE (μFE) models generated from HR-pQCT image data. During the DRF healing process, significant longitudinal changes were found in μFE-estimated stiffness, patient-reported outcomes, grip strength, range of motion (ROM), tenderness, number of cortices healed based on radiographs, and fracture line visibility (p < 0.05); however, no significant change was detected in HR-pQCT based total bone mineral density. Patient-reported outcomes, such as the Patient-Rated Wrist Evaluation (PRWE) and the Quick Disability of the Arm, Shoulder and Hand (QuickDASH) questionnaire, correlated strongly with μFE-estimated stiffness (0.61 ≥ rm ≥ 0.66). Based on μFE-estimated stiffness, PRWE and QuickDASH are the best predictors of stiffness recovery (p < 0.05) and may be used to guide duration of cast immobilization in the clinical setting.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it