Longitudinal changes in adiposity following anterior cruciate ligament reconstruction and associations with knee symptoms and function
Bibliographic record
Abstract
Objective: To evaluate adiposity after anterior cruciate ligament reconstruction (ACLR): i) cross-sectionally (1-year post-ACLR) compared to uninjured controls; ii) longitudinally up to 5 years post-ACLR; and iii) associations with patient-reported symptoms and physical performance. Methods: In 107 individuals post-ACLR and 19 controls, we assessed global (BMI), peripheral (subcutaneous adipose tissue thickness on the posteromedial side of knee MRI), and central (waist circumference in ACLR group) adiposity. Patient-reported symptoms (Knee injury and Osteoarthritis Outcome Score) and physical performance (hop for distance) were evaluated at 1 and 5 years post-ACLR. Linear regression models evaluated adiposity between groups. Paired t-tests evaluated changes in adiposity from 1- to 5 years post-ACLR. Linear regression models analyzed adiposity's associations with patient-reported symptoms and physical performance at 1-year post-ACLR, changes in symptoms and performance over 4 years post-ACLR, and longitudinal changes in adiposity and symptoms and performance, controlling for age, sex, and activity level. Results: ) and central (5 cm) adiposity, and lower average peripheral adiposity (1.3 mm) were observed. In general, adiposity at one-year post-ACLR was negatively associated with patient-reported symptoms and physical performance, and changes from 1 to 5 years post-ACLR. Increases in adiposity were negatively associated with changes in patient-reported symptoms and physical performance over four years post-ACLR. Conclusion: Greater global and central adiposity is a feature of young adults following ACLR and influences current and future patient-reported symptoms and physical performance.
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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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".