Correlation between subcutaneous fat measurements in knee MRI and BMI: relationship to obesity and related co-morbidities
Bibliographic record
Abstract
BACKGROUND: Obesity is a major public health disorder associated with multiple co-morbidities. Knee magnetic resonance imaging (MRI) permits visualization of the subcutaneous fat anatomy, which can be correlated to body mass index (BMI) and obesity-related co-morbidities. PURPOSE: This study intends to validate a method of correlating measurements of subcutaneous fat around the distal femur on axial MR images to BMI and obesity-related co-morbidities. MATERIAL AND METHODS: The most proximal axial slice of each knee MRI was divided into four quadrants. Measurements of the thickest portion of the subcutaneous fat in each quadrant were independently obtained, yielding a value which was assigned the name of the SubCut fat index. The relationship between the SubCut fat index of each quadrant and the patient's BMI was then evaluated. Receiver operating characteristic curves utilizing both the subcutaneous fat in the medial and lateral quadrants as well as BMI were performed with respect to obesity-related co-morbidities. RESULTS: = 0.01). Additionally, BMI and medial quadrants SubCut indices showed strong association with obesity-related co-morbidities including sleep apnea, asthma, diabetes, hypertension, gastroesophageal reflux disease, and osteoporosis. CONCLUSION: The SubCut fat index, a marker of distal femur subcutaneous fat on axial MRI, correlates with severity of obesity (BI) and associated obesity-related co-morbidities.
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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 | 0.005 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".