Tibiofemoral Contact Measures During Standing in Toe-In and Toe-Out Postures
Bibliographic record
Abstract
Knee osteoarthritis is thought to result, in part, from excessive and unbalanced joint loading. Toe-in and toe-out gait modifications produce alterations in external knee joint moments, and some improvements in pain over the short- and long-term. The aim of this study was to probe mechanisms of altered joint loading through the assessment of tibiofemoral contact in standing with toe-in and toe-out positions using an open magnetic resonance scanner. In this study, 15 young, healthy participants underwent standing magnetic resonance imaging of one of their knees in 3 foot positions. Images were analyzed to determine contact in the tibiofemoral joint, with primary outcomes including centroid of contact and contact area for each compartment and overall. The centroid of contact shifted laterally in the lateral compartment with both toe-in and toe-out postures, compared with the neutral position (P < .01), while contact area in the medial and lateral compartments showed no statistical differences. Findings from this study indicate that changes in the loading anatomy are present in the tibiofemoral joint with toe-in and toe-out and that a small amount of lateralization of contact, especially in the lateral compartment, does occur with these altered lower limb orientations.
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 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 | 0.001 | 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) | 0.002 | 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".