Association of Knee Effusion Detected by Physical Examination With Bone Marrow Lesions: Cross‐Sectional and Longitudinal Analyses of a Population‐Based Cohort
Bibliographic record
Abstract
OBJECTIVE: To determine the association of effusion detected by physical examination with the prevalence of bone marrow lesions (BMLs) on magnetic resonance imaging (MRI), and the incidence/progression of BMLs over 3 years in subjects with knee osteoarthritis. METHODS: A population-based cohort with knee pain (n = 255) was assessed for effusion on physical examination. On MRI, BMLs were graded 0-3 (none, mild, moderate, severe), and incidence/progression was defined as a worsening of the sum of BML scores over 6 surfaces by ≥1 grade. We analyzed the full cohort and a mild disease subsample with a Kellgren/Lawrence (K/L) grade <3. Cross-sectional logistic and longitudinal exponential regression analyses were performed, adjusted for age, sex, body mass index (BMI) and pain. We calculated sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for effusion detected by physical examination versus BMLs (prevalence and incidence/progression). RESULTS: The weighted mean age was 56.7 years, the mean BMI was 26.5, 56.3% were women, 20.1% had effusion on physical examination, and 80.7% had a K/L grade <3. Effusion on physical examination was significantly associated with prevalent BMLs in the full cohort (odds ratio [OR] 6.10 [95% confidence interval (95% CI) 2.77-13.44]) and in the K/L grade <3 cohort (OR 6.88 [95% CI 2.76-17.15]). In the full cohort, sensitivity, specificity, PPV, and NPV were 34.6, 92.5, 79.9, and 62.1%, respectively, and in the K/L <3 cohort 31.7, 94.0, 75.5, and 70.1%, respectively. Longitudinally, effusion on physical examination was not significantly associated with BML incidence/progression in the full cohort (hazard ratio [HR] 1.83 [95% CI 0.95-3.52]) or in the K/L grade <3 cohort (HR 1.73 [95% CI 0.69-4.33]). In the two cohorts, sensitivity, specificity, PPV, and NPV were 32.0, 82.2, 42.2, and 74.9%, respectively, and 21.2, 85.6, 30.1, and 78.8% respectively. CONCLUSION: BMLs on MRI can be predicted from physical examination effusion cross-sectionally, with a high PPV of 79.9%. Assessment for knee effusion on physical examination is useful for determining potential candidates with BMLs before costly MRI screening for recruitment into clinical trials.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".