Traction Loading Human Coracohumeral Ligaments With 20 and 40 Newton Forces and Sustained Creep Deformation: A Preliminary Cadaveric Investigation
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study was to determine if different force magnitudes, loading cycle repetitions, and loading speeds alter creep deformation (CD) during cyclical traction loading of human cadaveric coracohumeral ligaments (CHL). METHODS: Fifteen unembalmed human cadaveric CHL specimens were assigned to 3 groups based on maximal force and loading speed: (1) 40Nslow: 40N; 0.83 mm/s; (2) 20Nslow: 20N; 0.83 mm/s; (3) 20Nfast: 20N; 2.5 mm/s. All specimens underwent 360 cycles of traction loading in a material testing system. The material testing system collected CD during loading at 60-cycle intervals. Micrometer measurements determined CD 60 minutes after loading. Friedman's ANOVA was used to compare within-group CD changes, and Kruskal-Wallis ANOVA was used to compare between-group CD differences. RESULTS: All groups demonstrated increased CD during 360 cycles (P < .003) without differences between groups at any 60-cycle interval (P > .05). Sixty minutes after loading, CD was 7% (±5) in the 20Nslow, 15% (±12) in the 40Nslow, and 13% (±7) in the 20Nfast groups without between-group differences (P = .353). CONCLUSION: Cyclical traction loading cadaveric CHL specimens with 20N and 40N forces increased CD without a difference between groups. Creep deformation was partially retained 60 minutes after loading. No CD differences were found using 20N loads at 2 different loading speeds.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".