Bibliographic record
Abstract
Dyslipidaemias encompass about two dozen relatively rare monogenic disorders and syndromes for which the genetic basis has largely been defined. In addition, the complex polygenic basis of disturbed lipids and lipoproteins has been characterised in many patients, and has been shown to result from accumulation of many common polymorphisms with small effects on lipids. Genetic technologies, including dedicated genotyping and sequencing methods can detect both rare and common DNA variants underlying dyslipidaemias. Some dyslipidaemias may be clinically silent for years, but early diagnosis, including genetic diagnosis, may permit early intervention to prevent or delay deleterious downstream clinical consequences, such as premature vascular disease or acute pancreatitis. The potential clinical utility of genetic testing for familial hypercholesterolaemia, familial chylomicronaemia syndrome, lysosomal acid lipase deficiency and some others will increase demand for reliable genetic diagnostic methods. We review some current technologies, such as targeted next-generation sequencing that seem to be helpful with DNA diagnosis of dyslipidaemias. We also address technical, biological and clinical limitations of genetic testing in dyslipidaemias. Finally, genetic counselling issues, the potential impact of results on patients and health care providers, current gaps and future directions will be discussed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".