The Effectiveness of Wrist Guards for Reducing Wrist and Elbow Accelerations Resulting from Simulated Forward Falls
Bibliographic record
Abstract
The effectiveness of wrist guards and modifying elbow posture for reducing impact-induced accelerations at the wrist and elbow, for the purpose of decreasing upper extremity injury risk during forward fall arrest, has not yet been documented in living people. A seated human pendulum was used to simulate the impact conditions consistent with landing on outstretched arms during a forward fall. Accelerometers measured the wrist and elbow response characteristics of 28 subjects following impacts with and without a wrist guard, and with elbows straight or slightly bent. Overall, the wrist guard was very effective, with significant reductions in peak accelerations at the elbow in the axial and off-axis directions, and in the off-axis direction at the wrist by almost 50%. The effect of elbow posture as an intervention strategy was mixed; a change in magnitude and direction of the acceleration response was documented at the elbow, while there was little effect at the wrist. Unique evidence was presented in support of wrist guard use in activities like in-line skating where impacts to the hands are common. The elbow response clearly shows that more proximal anatomical structures also need to be monitored when assessing the effectiveness of injury prevention strategies.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".