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 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".