Improvements in radiographic and clinical assessment of distal radius fracture healing by FE-estimated bone stiffness
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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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".