Calf skinfold measurements as a diagnostic tool for lipodystrophy syndromes: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Lipodystrophy syndromes (LS) are characterized by reduced body fat and associated metabolic complications, such as insulin resistance and diabetes. The diagnosis of LS can be challenging in clinical settings. Skinfold measurement and body composition assessment using DEXA are commonly used for screening and diagnosis. This study aimed to identify alternative easy-to-use body composition parameters for diagnosing LS in clinical settings. METHODS: This cross-sectional study included patients with genetically confirmed congenital generalized lipodystrophy (CGL) and familial partial lipodystrophy (FPL), with 62 matched healthy controls. Comprehensive data, including medical history, body composition, laboratory measurements, and imaging parameters, were collected. The receiver operating characteristic (ROC) curve was constructed using genetic test results, the gold standard for diagnosing genetic lipodystrophy, as the reference, and the sensitivity and specificity of calf skinfold measurements were measured with those of the genetic tests. RESULTS: We included 62 patients with a mean age of 32 ± 18 (2-68) years, of whom 69.3% were women. The prevalence of diabetes mellitus (70%) and hypertriglyceridemia (83%) were also high. All body composition parameters significantly differed, except for calf skinfold measurements, which were similar between the CGL and FPL patients (p = 0.270). The ROC curve analysis identified a new cutoff point for calf skinfold measurement of <8 mm, with high diagnostic accuracy. CONCLUSIONS: This study identified calf skinfold measurements <8 mm as an accurate diagnostic tool for CGL and FPL. Notably, clinical accessibility to calf skinfold will favor its widespread use, standardization, and inclusion in physical examination protocols, improving lipodystrophy detection and diagnosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".