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Record W3215731582 · doi:10.1097/med.0000000000000695

Advances in the care of lipodystrophies

2021· article· en· W3215731582 on OpenAlexaff

Bibliographic record

VenueCurrent Opinion in Endocrinology Diabetes and Obesity · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNuclear Structure and Function
Canadian institutionsWestern University
Fundersnot available
KeywordsDyslipidemiaMEDLINEPopulationHealth carePatient safetyPatient care

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Lipodystrophies are a group of rare, heterogeneous disorders characterized by a lack or maldistribution of adipose tissue. Treatment focusses on the management of complications, including hypertriglyceridemia, which can be severe. Patients are predisposed to early atherosclerotic cardiovascular disease and acute pancreatitis. This review summarizes the recent advances in the treatment of lipodystrophies, with a particular focus on the treatment of hypertriglyceridemia in familial partial lipodystrophy (FPLD). RECENT FINDINGS: Treatment of dyslipidemia in FPLD requires management of secondary exacerbating factors, particularly insulin resistance and diabetes, together with modification of atherosclerotic cardiovascular disease risk factors. In addition, specific lipid-lowering therapies are usually needed, starting with statins and fibrates. Leptin therapy improves triglycerides. Several emerging treatments for hypertriglyceridemia include apo C-III antagonists (volanesorsen, AKCEA-APOCIII-LRx and ARO-APOC3) and angiopoietin-like 3 antagonists (evinacumab, vupanorsen and ARO-ANG3); efficacy observed in clinical trials of these agents in nonlipodystrophic patients with severe hypertriglyceridemia suggests that they may also be helpful in lipodystrophy. SUMMARY: Emerging therapies for dyslipidemia show promise in advancing the care of patients with lipodystrophy. However, these treatments are not yet approved for use in lipodystrophy. Further study of their efficacy and safety in this patient population is needed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.117
Threshold uncertainty score0.210

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.271
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2021
Admission routes1
Has abstractyes

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