Gait mechanics after ACL reconstruction: implications for the early onset of knee osteoarthritis
Bibliographic record
Abstract
BACKGROUND: Individuals who sustain a rupture of the anterior cruciate ligament (ACL) are at an increased risk for developing early-onset knee osteoarthritis (OA). The mechanism behind the early onset of the disease is still unknown. Knee OA progression has been previously examined by calculating the internal knee-abduction moment during gait. However, knee-joint moments have not been examined in individuals after ACL reconstruction as a potential mechanism for disease progression in early knee OA. OBJECTIVE: To determine if individuals who have undergone ACL reconstruction exhibit altered gait mechanics that may be associated with knee OA progression. METHODS: In total, 17 people who had previously undergone ACL reconstruction were enrolled in the study. A matched control group was recruited for comparison. All participants underwent gait analysis at an intentional walking speed to examine variables previously associated with knee OA progression, primarily the internal peak knee-abduction moment, during gait. One way ANOVAs were performed to examine differences in gait mechanics between the two groups. All joint moments were calculated as internal moments. RESULTS: The peak knee-abduction moment was increased by 21% in the ACL compared with the control group (p = 0.04). No other differences were seen in frontal plane knee or hip mechanics. CONCLUSION: It seems that individuals who have undergone ACL reconstruction exhibit an increased peak knee-abduction moment that may establish a potential mechanism of the earlier onset of knee OA in this population.
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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 | 0.002 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".