Age, sex, body anthropometry, and ACL size predict the structural properties of the human anterior cruciate ligament
Bibliographic record
Abstract
Anterior cruciate ligament (ACL) injury continues to be at the forefront of sports injury concerns because of its impact on quality of life and joint health prognosis. One strategy is to reduce the occurrence of this injury by identifying at-risk subjects based on key putative risk factors. The purpose of our study was to develop models that predict the structural properties of a subject's ACL based on the combination of known risk factors. We hypothesized that the structural properties of the ACL can be predicted using a multi-linear regression model based on significant covariates that are associated with increased risk of injury, including age, sex, body size, and ACL size. We also hypothesized that ACL size is a significant contributor to the model. The developed models had predictive capabilities for the structural properties of the ACL: load at failure (R2 = 0.914), elongation at failure (R2 = 0.872), energy at failure (R2 = 0.913), and linear stiffness (R2 = 0.756). Furthermore, sex, age, body mass, BMI, and height were contributors (p < 0.05) to all predicted structural properties. ACL minimal area was a contributor to elongation, energy at failure, and linear stiffness (p < 0.05), but not to load at failure. ACL volume was also a contributor to elongation and energy at failure (p < 0.05), but not to linear stiffness and load at failure models. ACL length was not a significant contributor to any structural property. The clinical significance of this research is its potential, after continued development and refinement of the model, for application to prognostic studies that are designed to identify individuals at increased risk for injury to the ligament.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".