Patellofemoral Bone Marrow Lesions: Natural History and Associations With Pain and Structure
Bibliographic record
Abstract
OBJECTIVE: To describe the natural history of patellofemoral (PF) joint bone marrow lesions (BMLs) over 2.6 years and associations between changes in PF joint BMLs, knee pain, and knee cartilage morphology in older adults over 5 years. METHODS: A prospective population-based cohort study of men and women ages 50-80 years (mean age 63 years, n = 406) was performed. PF joint BMLs, knee cartilage volume, and cartilage defect scores (range 0-4) were measured using the Whole-Organ Magnetic Resonance Imaging Score system at baseline and at 2.6 years. Knee pain was assessed by Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores at baseline and at 5 years. RESULTS: At baseline, 27% (n = 109) had PF joint BMLs; 24% of these increased (WOMAC score change of ≥1) at followup, 44% persisted, 32% decreased, and 21% resolved completely. Of those without PF joint BMLs at baseline, 20% of participants developed PF joint BMLs over 2.6 years. In multivariable analyses, a change in PF joint BMLs was deleteriously associated with a change in total knee pain (β = 0.67, 95% confidence interval [95% CI] 0.03, 1.31) and knee pain when going up/down stairs (β = 0.24, 95% CI 0.04, 0.44) over 5 years. Baseline PF joint and tibiofemoral joint cartilage volume were protective for PF joint BMLs (relative risk [RR] 0.69, 95% CI 0.52, 0.90 for PF joint), while baseline PF joint cartilage defects were associated with an increase in PF joint BMLs (RR 1.73, 95% CI 1.38, 2.17) over 2.6 years. TF joint cartilage defects were not associated with increases in PF joint BMLs. CONCLUSION: PF joint BMLs are not static, and change is clinically relevant. PF joint cartilage morphology predicts increases in PF joint BMLs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".