Characterization of the Fatigue Behavior of the Medial Collateral Ligament Utilizing Traditional and Novel Mechanical Variables for the Assessment of Damage Accumulation
Bibliographic record
Abstract
Ligaments are regularly subjected to repetitive loading in vivo. Typically, mechanical studies focus on repetitive loading protocols of short duration, while those characterizing damage accumulation over a longer duration (i.e., fatigue studies) are lacking. The aims of this study were as follows: (a) to demonstrate that damage does accumulate in ligament tissue subjected to repetitive loading and (b) to evaluate existing and new methods for characterizing fatigue damage accumulation. It was hypothesized that ligaments would accumulate damage with repetitive loading as evidenced by failure at stresses well below ultimate tensile strength, creep curve discontinuities, and by reductions in stiffness during loading. Eight normal medial collateral ligaments from female New Zealand white rabbits were cycled in tension, between 0 MPa and 28 MPa, to failure or until 259,200 cycles, whichever came first. Medial collateral ligaments that did not fail were subsequently loaded to failure. Displacement rates (dl(max)/dt) as well as primary, secondary, and tertiary creeps were monitored as indices of damage accumulation and impending mechanical failure. Additionally, the relative utilities of tangent, secant, and chord stiffness parameters were critically evaluated. Finally, new uses for the second derivative of force-displacement data were explored. Three out of eight ligaments failed during testing, demonstrating that ligaments can fail in fatigue under moderate tensile stress in vitro. The evaluation of displacement rates (dl(max)/dt), as well as primary through tertiary creep patterns, were not well suited to predicting failure in normal ligaments until rupture was all but imminent. Tangent stiffness, which was calculated from a mathematically defined start of the "linear region," was surprisingly constant throughout testing. Secant stiffness dropped in a predictable fashion, providing a global indicator of tissue stiffness, but did not provide any insight into fiber mechanics. Chord stiffness, on the other hand, appeared to be sensitive to fiber recruitment patterns. The second derivative of force-displacement data proved to be a useful means of (a) objectively defining the start of the linear region and (b) inferring changes in fiber recruitment patterns within ligament tissue. Tangent, secant, and chord stiffnesses highlight different attributes of ligament responses to loading; hence these parameters cannot be used interchangeably. Additionally, the second derivative of the force-displacement curve was introduced as a useful descriptive and analytical tool.
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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 | 0.000 | 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".