Association of baseline MRI-defined structural features with knee symptom trajectories over nine years: Data from the osteoarthritis initiative (OAI)
Bibliographic record
Abstract
OBJECTIVE: The development of knee symptoms in OA over time can present with multiple phenotypes. This study aims to identify MRI-defined predictors of trajectories of knee symptoms. DESIGN: All knees in the Osteoarthritis Initiative (OAI) with at least one baseline score of 0 from one of four knee symptom measurements were included. These measurements included pain severity, knee pain frequency, and the Western Ontario and McMaster Universities Osteoarthritis Index knee pain and knee function scores. The latent class mixed model (LCMM) was employed to categorize knees into different trajectories and to assess their association with various MRI-defined structural features while controlling for important covariates. RESULTS: A total of 1221 knees were grouped into two classes: 596 with a minimal knee symptom trajectory and 625 with a rapidly progressive trajectory. The class-membership model revealed that the following variables were associated with being in the rapidly progressive phenotype: bone marrow lesions (BMLs) score (Odds Ratio (OR) of 1.12 [95 % Confidence Interval (CI): 1.03, 1.22]) for each unit increase in BML total size scores; females, widespread pain, CES-D, overweight, and obesity. The age group 65 to 79 vs. that of 45 to 55 and highest year of education completed were less likely to be associated with the rapidly progressive knee pain group. Other covariates were not significant. CONCLUSION: Two distinct phenotypes/trajectories were consistently identified across all four knee symptom measurements in a multi-trajectory model. Modifiable factors such BMLs, BMI, widespread pain and depression may serve as indicators of higher risk of rapid knee symptom progression.
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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 | 0.002 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".