Longitudinal Changes in Magnetic Resonance Imaging–Based Measures of Femorotibial Cartilage Thickness as a Function of Alignment and Obesity: Data From the Osteoarthritis Initiative
Bibliographic record
Abstract
OBJECTIVE: To investigate the interaction between malalignment and body mass index (BMI) on cartilage thickness change in patients with knee osteoarthritis (OA). METHODS: Femorotibial cartilage thickness was measured from baseline to 2 years in 558 knees with radiographic OA. Cartilage thickness was determined in the central weight-bearing medial femorotibial cartilage (cMFTC) and lateral (cLFTC) compartments. Femorotibial angle (FTA) was stratified into neutral, minor, and definite malalignment. BMI was stratified using World Health Organization classifications for normal, overweight, and obese. Multivariable linear regression models were used to investigate the interaction between alignment and BMI, adjusting for age, sex, and disease severity. RESULTS: There was no significant interaction for continuous measures of alignment and BMI (P = 0.301 for cMFTC and P = 0.852 for cLFTC). Using BMI tertiles, the association between alignment and medial or lateral cartilage thickness loss was not moderated by BMI, despite a significant association of malalignment with greater cartilage thickness loss (P ≤ 0.005). Using FTA tertiles, the association between BMI and medial cartilage thickness loss was approximately 3 times greater in knees with definite malalignment (P = 0.149) and approximately 5 times greater in knees with minor malalignment (P = 0.006). Specifically, knees with minor varus significantly modified this relationship (P = 0.021). CONCLUSION: Malalignment was significantly associated with cartilage thickness loss per degree increase in malalignment, but was not moderated by BMI. BMI was significantly associated with greater rates of medial cartilage thickness loss per unit increase in BMI but only in knees with minor varus malalignment. These findings have implications for better understanding patient subgroups and intervention strategies targeting risk factors for knee OA.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".