Natural history and clinical significance of MRI-detected bone marrow lesions at the knee: a prospective study in community dwelling older adults
Bibliographic record
Abstract
INTRODUCTION: There are conflicting data on the natural history and clinical significance of bone marrow lesions (BMLs). The aims of this study were to describe the natural history of MRI-detected BMLs at the knee using a quantitative measure and examine the association of BMLs with pain, function and stiffness scores, and total knee replacement (TKR) surgery. METHODS: A total of 395 older males and females were randomly selected from the general population (mean age 63 years, range 52 to 79) and measured at baseline and approximately 2.7 years later. BMLs were determined using T2-weighted fat saturation MRI by measuring the maximum area of the lesion. Reproducibility was excellent (intraclass correlation coefficient (ICC): 0.97). Pain, function, and stiffness were assessed by Western Ontario and McMaster Universities Osteoarthritis (WOMAC) scores. X-ray was used to assess radiographic osteoarthritis (ROA) at baseline. RESULTS: At baseline, 43% (n = 168/395) had a BML. Of these 25% decreased in size and 24% increased. Of the remaining sample (n = 227), 7% developed a new BML. In a multivariable model, a change in BML size was associated with a change in pain and function scores (β = 1.13 to 2.55 per 1 SD increase, all P < 0.05), only in those participants without ROA. Lastly, baseline BML severity predicted TKR surgery (odds ratio (OR) 2.10/unit, P = 0.019). CONCLUSIONS: In a population based sample, BMLs (assessed by measuring maximal area) were not static, with similar proportions both worsening and improving. A change in BML size was associated with changes in pain in those without established ROA. This finding suggests that fluctuating knee pain may be attributable to BMLs in those participants with early stage disease. Baseline BMLs also predicted TKR surgery. These findings suggest therapeutic interventions aimed at altering the natural history of BMLs should be considered.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".