Knee kinesiography for dynamic assessment of anterior cruciate ligament ruptures: Potential for a pediatric application
Bibliographic record
Abstract
Anterior cruciate ligament (ACL) ruptures are common, particularly among athletes, and lead to altered biomechanics that complicate rehabilitation and return to activities. These alterations must be assessed to tailor therapeutic strategies and reduce the risk of long-term complications. This literature review examines the clinical applications of knee kinesiography, focusing on its role in evaluating dynamic knee adaptations following ACL rupture. It highlights the method’s advantages over traditional motion analysis techniques and explores its potential use in pediatric populations. This literature review was conducted in February 2025 in PubMed, Google Scholar, and Web of Science without date restrictions. Inclusion criteria were peer-reviewed studies involving patients with ACL ruptures and assessing knee biomechanics using knee kinesiography. Studies using laboratory-based motion capture systems, other portable technologies, case reports, or lacking a clear description of gait assessment methods were excluded. Five cohort studies were included: four in adults with ACL deficiency and one in pediatric patients. Knee kinesiography allows objective, dynamic, weight-bearing assessment of knee kinematics across all three anatomical planes. It detects gait alterations post-ACL rupture, such as increased stance-phase knee flexion and abnormal tibial rotation. It supports personalized rehabilitation based on objective data. However, pediatric applications remain limited, and the lack of normative data restricts interpretation in this population. Knee kinesiography is a valuable, accessible tool for dynamic analysis following ACL rupture. Clinically, it can guide individualized treatment strategies. Further pediatric research is needed to establish normative values and adapt this approach to younger populations.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".