Adiposity and cartilage lesions following ACL reconstruction
Bibliographic record
Abstract
Objective To determine if global, central, or peripheral adiposity is associated with prevalent and worsening cartilage lesions following anterior cruciate ligament reconstruction (ACLR). Methods In 107 individuals one-year post-ACLR, adiposity was assessed globally (body mass index, BMI), centrally (waist circumference), and peripherally (knee subcutaneous adipose tissue thickness) from magnetic resonance imaging (MRI). Tibiofemoral and patellofemoral cartilage lesions were assessed from knee MRIs at 1- and 5-years post-ACLR. Poisson regression evaluated the relation of adiposity with prevalent and worsening tibiofemoral and patellofemoral cartilage lesions adjusting for age, sex, and activity level. Results The prevalence ratios of adiposity with tibiofemoral (presence in 49%) and patellofemoral (44%) cartilage lesions ranged from 0.99 to 1.03. Adiposity was more strongly associated with longitudinal changes in tibiofemoral (worsening in 21%) and patellofemoral (44%) cartilage lesions. One-unit increase in global (kg/m 2 ), central (cm), and peripheral (mm) adiposity was associated with a higher risk of worsening tibiofemoral cartilage lesions by 17% (risk ratios [95% CI]: 1.17 [1.09 to 1.23]), 5% (1.05 [1.02 to 1.08]), and 9% (1.09 [1.03 to 1.16]), and patellofemoral cartilage lesions by 5% (1.05 [1.00 to 1.12]) 2% (1.02 [1.00 to 1.04]) and 2% (1.02 [1.00 to 1.04]), respectively. Conclusion Greater adiposity was a risk factor for worsening cartilage lesions up to 5 years post-ACLR. Clinical interventions aimed at mitigating excess adiposity may be beneficial in preventive approaches for early post-traumatic OA.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".