Baseline knee adduction moment interacts with body mass index to predict loss of medial tibial cartilage volume over 2.5 years in knee Osteoarthritis
Bibliographic record
Abstract
ABSTRACT This study aimed to determine the extent to which changes over 2.5 years in medial knee cartilage thickness and volume were predicted by: (1) Peak values of the knee adduction (KAM) and flexion moments; and (2) KAM impulse and loading frequency, representing cumulative load, after controlling for age, sex and body mass index (BMI). Adults with clinical knee osteoarthritis participated. At baseline and approximately 2.5 years follow‐up, cartilage thickness and volume of the medial tibia and femur were segmented from magnetic resonance imaging scans. Gait kinematics and kinetics, and daily knee loading frequency were also collected at baseline. Multiple linear regressions predicted changes in cartilage morphology from baseline gait mechanics. Data were collected from 52 participants (41 women) [age 61.0 (6.9) y; BMI 28.5 (5.7) kg/m2] over 2.56 (0.51) years. There were significant KAM peak‐by‐BMI (p = 0.023) and KAM impulse‐by‐BMI (p = 0.034) interactions, which revealed that larger joint loads in those with higher BMIs were associated with greater loss of medial tibial cartilage volume. In conclusion, with adjustments for age, sex, and cartilage measurement at baseline, large magnitude KAM peak and KAM impulse each interacted with BMI to predict loss of cartilage volume of the medial tibia over 2.5 years among individuals with knee osteoarthritis. These data suggest that, in clinical knee osteoarthritis, exposure to large KAMs may be detrimental to cartilage in those with larger BMIs. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 35:2476–2483, 2017.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".