Frequency of Bone Marrow Lesions and Association with Pain Severity: Results from a Population-based Symptomatic Knee Cohort
Bibliographic record
Abstract
OBJECTIVE: To evaluate the prevalence of bone marrow lesions (BML) and their association with pain severity in a population-based cohort of symptomatic early knee osteoarthritis (OA). METHODS: Subjects with knee pain (n = 255), age 40-79 years, were evaluated by radiograph and magnetic resonance imaging (MRI) and classified into OA stages: no OA (NOA), preradiographic OA (PROA), and radiographic OA (ROA). BML were graded 0-3 (none, mild, moderate, severe) in 6 regions and defined as (1) BMLsum = the sum of 6 scores; and (2) BMLmax = the worst score at any region. Pain was assessed by the Western Ontario and McMaster Universities OA Index (WOMAC). Linear regression analysis was completed to assess the association of Total WOMAC Pain (primary outcome) versus BMLsum or BMLmax. Secondary outcomes were WOMAC Pain on Walking and WOMAC Pain on Climbing Stairs. All analyses were adjusted for age, sex, body mass index, OA stage, joint effusion, and meniscal damage. RESULTS: BML were present in 11% of NOA, 38% of PROA, and 71% of ROA subjects (p < 0.001). No association was seen for BMLsum or BMLmax versus Total WOMAC Pain or Pain on Walking. However, BMLsum was associated with Pain on Climbing Stairs [regression coefficients (RC) = 0.09, 95% CI 0.00-0.18]. BMLmax was associated with Pain on Climbing Stairs, with the strongest association for severe BML (RC 0.60, 95% CI 0.04-1.17). CONCLUSION: BML were present in 38% of PROA and 71% of ROA subjects in this symptomatic knee cohort. BML were significantly associated with Pain on Climbing Stairs but not Total WOMAC or Pain on Walking.
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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 | 0.001 | 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".