Off‐axis loads cause failure of the distal radius at lower magnitudes than axial loads: A side‐to‐side experimental study
Bibliographic record
Abstract
Off-axis loading associated with a fall onto the outstretched hand has been hypothesized to induce distal radius failure at lower magnitudes than axially directed loading commonly used in biomechanical models for estimating fracture risk. However, this hypothesis has not been tested with side-to-side experimental testing. The objective of this study was to compare distal radius failure loads between forearm pairs experimentally tested in an axial or off-axis loading configuration. We acquired 18 pairs of cadaveric forearms from 18 female donors (mean age (standard deviation): 84.4 (7.9) years). Each forearm pair was tested to failure using either an axial compression test (vertical orientation with 0° dorsal inclination, 3°-6° radial inclination) or an off-axis test corresponding to the hand position during a fall (15° dorsal inclination, 3°-6° radial inclination). Failure testing was performed at 3 mm/s onto the palm of the hand until fracture occurred. Of the 18 pairs, 11 sustained a distal radius fracture. We compared failure loads between the two groups using a paired t test. Results indicated that failure load under off-axis loading was 29% lower than failure load under axial compressive loading (mean difference: -0.31 kN; 95% confidence interval: -0.47 to -0.16 kN, P = .001). In conclusion, off-axis loading associated with a fall onto the outstretched hand resulted in a 29% lower failure load. Integrating an off-axis loading configuration into current biomechanical models of distal radius bone strength may prevent overestimating of failure load and may offer a clinically relevant option to estimate distal radius fracture risk and monitor therapy efficacy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".