The Influence of Knee Position on Ultrasound Imaging of Femoral Cartilage in Individuals with Anterior Cruciate Ligament Reconstruction
Bibliographic record
Abstract
Background Articular cartilage is important for knee function and can be imaged using ultrasound. The purpose was to compare femoral cartilage thickness and echo intensity (EI) measured at 90° and 140° of knee flexion and between limbs in a cohort with unilateral anterior cruciate ligament reconstruction (ACLR). We also examined associations between gait biomechanics and cartilage outcomes. Methods Twenty-seven individuals with primary unilateral ACLR participated (12 men, 15 women; age = 22.3 ± 3.8 years; time since ACLR = 71.2 ± 47.2 months). Ultrasound was used to obtain femoral cartilage measurements. Gait outcomes included peak KFA (knee flexion angle) and peak external knee flexion moment (KFM). Cartilage outcomes were compared using a 2 ( position ) × 2 ( limb ) repeated measures ANOVA (analysis of variance). Gait and cartilage associations were assessed using linear regression. Findings There were no position × limb interactions for any cartilage outcome (all P > 0.05). Medial ( P = 0.038) and central cartilage ( P < 0.001) were thicker, whereas central ( P = 0.029) and lateral cartilage EI ( P = 0.003) were lower when measured at 90° than those at 140° of knee flexion. Medial cartilage was thicker in the ACLR than that in the contralateral limb ( P = 0.016). A larger KFM was associated with thicker medial cartilage (ΔR 2 = 0.146, P = 0.021) and central cartilage (ΔR 2 = 0.159, P = 0.039) measured at 140° of knee flexion in the ACLR limb but not at 90°. Interpretation Findings suggest that imaging position influences cartilage thickness and EI measurements in individuals with ACLR and should be considered in study designs and clinical evaluation. A greater KFM was associated with thicker cartilage within specific portions of the distal femur.
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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 | 0.000 |
| 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".