Comparing Methods of Quantifying Tibial Acceleration Slope
Bibliographic record
Abstract
Considerable variability in tibial acceleration slope (AS) values, and different interpretations of injury risk based on these values, have been reported. Acceleration slope variability may be due in part to variations in the quantification methods used. Therefore, the purpose of this study was to quantify differences in tibial AS values determined using end points at various percentage ranges between impact and peak tibial acceleration, as a function of either amplitude or time. Tibial accelerations were recorded from 20 participants (21.8 +/- 2.9 years, 1.7 m +/- 0.1 m, 75.1 kg +/- 17.0 kg) during 24 unshod heel impacts using a human pendulum apparatus. Nine ranges were tested from 5-95% (widest range) to 45-55% (narrowest range) at 5% increments. AS(Amplitude) values increased consistently from the widest to narrowest ranges, whereas the AS(Time) values remained essentially the same. The magnitudes of AS(Amplitude) values were significantly higher and more sensitive to changes in percentage range than AS(Time) values derived from the same impact data. This study shows that tibial AS magnitudes are highly dependent on the method used to calculate them. Researchers are encouraged to carefully consider the method they use to calculate AS so that equivalent comparisons and assessments of injury risk across studies can be made.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".