Accuracy of Ultrasound Imaging Technique for Assessing Lipoatrophy in HIV-Infected Subjects
Bibliographic record
Abstract
OBJECTIVE: To compare the accuracy of ultrasound imaging technique to that of clinical diagnosis in evaluating subcutaneous fat changes in HIV-infected subjects. METHODS: HIV-uninfected control subjects (Group A), HIV-infected subjects with clinically assessed lipoatrophy (Group B), and HIV-infected subjects without clinical lipoatrophy (Group C) underwent ultrasound measurements of subcutaneous fat thickness at facial, brachial and thigh regions. ROC curve analyses were used to estimate ultrasound prediction accuracy and cut-off values of subcutaneous fat thickness. RESULTS: 228 subjects were enrolled: 78 in Group A, 73 in Group B, and 77 in Group C. Facial lipoatrophy: ROC curve analysis identified optimal cut-off value of 13.3 mm [sensitivity, 96.0%; specificity, 76.9% AUC 0.92], 5.0 mm [sensitivity, 71.4%; specificity, 92.3%; AUC 0.90] and 11.2 mm [sensitivity, 95.8%; specificity, 89.7%; AUC 0.97] for females and 12.05 mm [sensitivity, 51.2%; specificity, 87.2%; AUC 0.74], 4.1 mm [sensitivity, 76.2%; specificity, 89.7%; AUC 0.85] and 4.35 mm [sensitivity, 60.0%; specificity, 89.7%; AUC 0.82] for males in assessing facial, brachial and crural lipoatrophy respectively. Using this cut-off values, 12/25 (48%) females and 17/49 (34.7%) males, 12/28 (42.9%) females and 23/49 (46.9%) males, 19/28 (67.9%) females and 12/49 (24.5%) males in Group C would be classified as "sub-clinical" facial, brachial and crural lipoatrophy respectively. CONCLUSIONS: The results of our study show that in the assessment of subtle subcutaneous fat changes ultrasound is more accurate than clinical evaluation and confirm the usefulness of ultrasound imaging technique in identifying lipoatrophy at an early stage.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".