Effects of cyclic loading on the mechanical properties and failure of human patellar tendon
Bibliographic record
Abstract
Patellar tendinopathy is a common overuse injury in sports such as volleyball, basketball, and long-distance running. Microdamage accumulation, in response to repetitive loading of the tendon, plays an important role in the pathophysiology of patellar tendinopathy. This damage presents mechanically as a reduction in Young’s modulus and an increase in residual strain . In this study, 19 human patellar tendon samples underwent cyclic testing in load control until failure, segmented by four ramped tests where digital image correlation (DIC) was used to assess anterior surface strain distributions. Ramped tests were performed prior to cyclic testing and at timepoints corresponding to 10%, 20%, and 30% of cyclic stiffness reduction . Young’s modulus significantly decreased and cyclic energy dissipation significantly increased over the course of cyclic testing. The DIC analysis illustrated a heterogeneous strain distribution, with strain concentrations increasing in magnitude and size over the course of cyclic testing. Peak stress and initial peak strain magnitudes significantly correlated with the number of cycles to failure ( r 2 = 0.65 and r 2 = 0.57, respectively, p < 0.001); however, the rates of peak cyclic strain and modulus loss displayed the highest correlations with the number of cycles to failure ( r 2 = 96% and r 2 = 86%, respectively, p < 0.001). The high correlation between the rates of peak cyclic strain and modulus loss suggest that non-invasive methods to continuously monitor tendon strain may provide meaningful predictions of overuse injury in the patellar tendon.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".