Bone marrow lesions in people with knee osteoarthritis predict progression of disease and joint replacement: a longitudinal study
Bibliographic record
Abstract
OBJECTIVES: The presence of bone marrow lesions (BMLs) has been linked to pain and progression of knee OA. The aim of this study was to determine the relationship between BMLs and longitudinal change in tibial cartilage volume and risk of knee joint replacement in subjects with knee OA. METHODS: One hundred and nine men and women with symptomatic knee OA were recruited. The same knee was imaged using MRI at baseline and ∼2 years later. Tibial cartilage volume and BMLs were measured. Knee joint replacement over 4 years was determined. RESULTS: The mean age of the subjects at baseline was 63.2 (s.d. 10.3) years. BMLs were present in 66% of the subjects. Cross-sectionally, BMLs were negatively associated with both medial (regression coefficient -121.4; 95% CI -183.8, -859.1; P<0.001) and lateral (regression coefficient -142.1; 95% CI -241.8, -42.4; P=0.01) tibial cartilage volume data. Longitudinally, for every 1-score increase in baseline BML severity (range 0-4), the annual total tibial cartilage loss was increased by 1.14% (95% CI 0.29%, 1.87%; P=0.01). The risk of knee joint replacement over 4 years increased with increasing BML score (odds ratio 1.57; 95% CI 1.04, 2.35; P=0.03). CONCLUSION: The prevalence and severity of BMLs are associated with less tibial cartilage volume and greater cartilage loss over 2 years. Moreover, severity of BMLs was positively associated with risk of knee joint replacement over 4 years. This provides further support for the importance of BMLs in identifying those with OA most likely to progress. Identifying factors that prevent or reduce the severity of BMLs may provide an important target in the prevention of disease progression and treatment of OA, and the subsequent need for arthroplasty.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".