Cartilage deformation following a walking bout in individuals with anterior cruciate ligament reconstruction
Bibliographic record
Abstract
Abstract The purpose was to (1) compare the effect of a walking bout on femoral cartilage deformation between limbs with and without anterior cruciate ligament reconstruction (ACLR) and (2) examine the association between gait kinetics and the magnitude of cartilage deformation. A total of 30 individuals with primary unilateral ACLR completed this study [14 male, 16 female; age = 22.57 (3.78) years; body mass index (BMI) = 25.88 (5.68) kg/m2; time since ACLR = 61.00 (16.43) months]. Overground walking biomechanics were assessed on day 1, and a 30‐min walking bout or 30‐min resting bout (control) were completed on days 2 and 3 (counterbalanced order). Femoral cartilage thickness was measured using ultrasound before, immediately following, and 30‐min following each intervention. Linear mixed effects models compared the effect of walking on cartilage thickness between the ACLR and contralateral limbs after adjusting for sex, BMI, speed, and the number of steps. Stepwise regression examined the association between the external knee flexion and adduction moments and cartilage deformation following walking. There was a significant limb × time interaction for medial cartilage thickness. Post hoc analyses indicated that cartilage thickness decreased immediately following walking in the contralateral but not ACLR limb. Main effects of limb were observed for medial, central, and lateral cartilage thickness indicating thicker cartilage in the ACLR compared with contralateral limb. A higher knee adduction moment was associated with greater cartilage deformation in the ACLR limb. Femoral cartilage in the ACLR limb exhibited a less dynamic response to walking than the uninvolved limb, which may be due to habitual underloading during gait.
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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 | 0.000 | 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 | 0.000 |
| 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".