Frontal Plane Knee Kinematics and Kinetics During Gait in Children and Youth with Achondroplasia—Correspondence with Static X-Ray Images and Relevance to Symptoms
Bibliographic record
Abstract
Background: Frontal knee malalignments are hallmarks of Achondroplasia (ACH), along with disproportional short stature. Typically, X-rays are used to assess them, but 3D gait analysis (3DGA) may additionally be used to evaluate dynamic knee function. The research questions were as follows: (1) What is the relationship between X-rays and 3DGA in ACH? (2) Do children with ACH have abnormal frontal knee kinematics and kinetics? (3) Are there aspects of 3DGA that relate to knee symptoms? Methods: A total of 62 knees of 31 children with ACH (age: 11.1 ± 4.3 years, 34 symptomatic knees) underwent 3DGA and X-ray as part of their standard clinical care. X-rays were analyzed for mechanical tibiofemoral angle (mTFA). Relationships between X-rays and 3DGA were determined. Sixty-two knees of 31 age-matched typically developing (TD) children served as references for 3DGA. Frontal knee kinematics (including thrust RoM) and adduction moments (KAMs) were compared. Multiple regression was performed for measurements associated with KAM, and ANOVA was used to compare TD and ACH knees with and without pain. Results: There was a high correlation between static frontal knee angles and mTFA (r = 0.93, p < 0.001, mean difference = −2.9°). ACH knees with a regular mTFA also showed significantly increased KAM. Multiple regression analysis showed that mTFA was the most relevant predictor of KAM (R2 = 0.41–0.75). Symptomatic knees (n = 34/62) experienced significantly more knee RoM in early stance than asymptomatic knees. Conclusions: Three-dimensional gait analysis may be an objective screening method for dynamic knee alignment and stability and may complement radiography in monitoring ACH. Symptoms may depend on knee thrust, while the impact of altered KAM needs further study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".