Predicting Distal Radius Bone Strains and Injury in Response to Impacts Using Multi-Axial Accelerometers
Bibliographic record
Abstract
Measuring a bone's response to impact has traditionally been done using strain gauges that are attached directly to the bone. Accelerometers have also been used for this purpose because they are reusable, inexpensive and can be attached easily. However, little data are available relating measured accelerations to bone injury, or to judge if accelerometers are reasonable surrogates for strain gauges in terms of their capacity to predict bone injuries. Impacts were applied with a custom designed pneumatic impact system to eight fresh-frozen human cadaveric radius specimens. Impacts were repeatedly applied with increasing energy until ultimate failure occurred. Three multiaxial strain gauge rosettes were glued to the bone (two distally and one proximally). Two multiaxial accelerometers were attached to the distal dorsal and proximal volar aspects of the radius. Overall, peak minimum and maximum principal strains were calculated from the strain-time curves from each gauge. Peak accelerations and acceleration rates were measured parallel (axial) and perpendicular (off-axis) to the long axis of the radius. Logistic generalized estimating equations were used to create strain and acceleration-based injury prediction models. To develop strain prediction models based on the acceleration variables, Linear generalized estimating equations were employed. The logistic models were assessed according to the quasi-likelihood under independence model criterion (QIC), while the linear models were assessed by the QIC and the marginal R(2). Peak axial and off-axis accelerations increased significantly (with increasing impact energy) across all impact trials. The best injury prediction model (QIC = 9.42) included distal resultant acceleration (p < 0.001) and donor body mass index (BMI) (p < 0.001). Compressive and tensile strains were best predicted by separate uni-variate models, including peak distal axial acceleration (R(2 )= 0.79) and peak off-axis acceleration (R(2 )= 0.79), respectively. Accelerometers appear to be a valid surrogate to strain gauges for measuring the general response of the bone to impact and predicting the probability of bone injury.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".