Lipodystrophy Severity Score to Assess Disease Burden in Lipodystrophy
Bibliographic record
Abstract
CONTEXT: Lipodystrophy syndromes are rare disorders characterized by deficient adipose tissue, leading to insulin resistance, dyslipidemia, and organ system abnormalities. OBJECTIVE: Our goal was to develop a lipodystrophy severity score (LDS) to holistically capture the diverse manifestations of lipodystrophy into a numerical score to aid in prediction of clinical outcomes and/or treatment impact. DESIGN: An 8-domain LDS was developed by 8 disease experts in consultation with patient organizations. The LDS was rated for feasibility and content validity by 28 additional clinicians and 9 patient representatives. LDS was compared to the Clinical Global Impression (CGI) of severity for 20 putative patient profiles, each at 2 different time points, and by comparing change in LDS to global impression of change. For external validation, LDS was calculated in 2 cohorts of patients with lipodystrophy treated with metreleptin. RESULTS: LDS domains include Diabetes/Insulin Resistance, Microvascular Complications of Diabetes, Lipids, Cardiovascular, Liver, Kidney, Reproductive, and Other. Each domain is assessed by 1 or more questions assessing both lifetime and recent complications of lipodystrophy. The LDS had high content validity and feasibility and high reliability by intraclass correlation coefficients (>0.95). Global and domain-specific LDS were strongly correlated with CGI, as were changes in scores across visits (R = 0.79-0.99, P < .001 for all). In generalized lipodystrophy, metreleptin significantly reduced LDS (from 46 to 26 at 12 months, P < .001). The reductions were smaller in partial lipodystrophy (from 65 to 61 at 12 months, P = .04). CONCLUSION: The LDS can reflect the severity of diverse manifestations of lipodystrophy and monitor changes following interventions.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".