Association of Body Mass Index With Incidence and Progression of Knee Effusion on Magnetic Resonance Imaging and on Knee Examination
Bibliographic record
Abstract
OBJECTIVE: To determine the association of body mass index (BMI) with incidence and progression of knee effusion on magnetic resonance imaging (MRI) and physical examination (PE) in a longitudinal cohort with knee pain. METHODS: A population-based cohort was assessed at baseline and 3 years (n = 163). BMI was categorized as normal (<25), overweight (25-29.9), and obese (≥30). Knee effusion was graded as 0-3 (absent/mild/moderate/severe) on MRI and 0-1 (absent/present) on PE. Progression of MRI effusion (MRIeff ) was an increase of ≥1 grade in those with grade 1 or 2 at baseline. Incident MRIeff and PE effusion (PEeff ) were any effusion at followup (>0) in those with grade 0 at baseline. A second type of incident MRIeff was effusion grade ≥2 at followup in those with grade <2 at baseline. Exponential regression analysis was used, adjusted for age, sex, and radiographic severity. RESULTS: Incident MRIeff ≥1, incident MRIeff ≥2, incident PEeff , and progression of MRIeff were seen in 14 of 73 (19%), 18 of 140 (13%), 26 of 127 (20%), and 18 of 86 (21%), respectively. There was a borderline statistical association of obesity with progression of MRIeff (hazard ratio [HR] 3.3 [95% confidence interval (95% CI) 1.0-11.2]) and with incident MRIeff ≥2 (HR 3.4 [95% CI 1.0-11.5]). BMI was not associated with incident MRIeff ≥1 (HR overweight 1.1 [95% CI 0.3-3.6], obese 1.0 [95% CI 0.2-5.0]). Overweight was associated with incident PEeff (HR 4.5 [95% CI 1.4-14.2]), while obesity was not statistically significant (HR 3.1 [95% CI 0.9-11.1]). CONCLUSION: Obesity was a risk factor for incident and progressive knee effusion in this population-based cohort. These findings highlight an important link between obesity and inflammation in knee osteoarthritis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".