Bibliographic record
Abstract
Abstract Hypertriglyceridemia (HTG) is a commonly encountered medical condition defined by elevated fasting plasma triglyceride (TG) levels. The degree of elevation may range from mild to severe, with clinical features ranging from asymptomatic to increased vascular disease susceptibility to life‐threatening pancreatitis. While numerous nongenetic secondary factors play a strong contributory role, a genetic component is frequently present in patients who clinically express HTG. Purely monogenic or Mendelian HTG – for example autosomal recessive chylomicronemia – is exceedingly rare and results from bi‐allelic mutations affecting lipolysis. The pool of patients with more common polygenic HTG has an increased frequency of heterozygous large‐effect rare variants in LPL (lipoprotein lipase) and related genes, together with a high burden of small‐effect common polymorphisms, although any particular variant is not definitively causative in this condition. Key Concepts Hypertriglyceridemia (HTG) ranges from mild to severe, with the role of genetic determinants increasing with a more severe clinical presentation. One definition proposes that plasma triglyceride (TG) levels in mild‐to‐moderate HTG are between 2.0 and 9.9 mmol L −1 (175 and 885 mg dL −1 ), while in severe HTG, levels exceed 10 mmol L −1 (885 mg dL −1 ). A gamut of secondary factors can contribute to clinical expression of HTG. Clinical consequences of HTG range from increased vascular disease risk to visible lipid eruptions on the skin to life‐threatening pancreatitis, depending on the affected species of lipoprotein particles and associated disturbances. Monogenic chylomicronemia is an extreme and rare form of severe HTG that results from bi‐allelic mutations in LPL , APOC2 , APOA5 , LMF1 , or GPIHBP1 genes. Most other HTG cases have a polygenic basis: this patient pool harbours an assortment of genetic variants, including a high burden of rare heterozygous large‐effect variants and common small‐effect variants.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".