Familial hypercholesterolemia: Review of diagnosis, screening, and treatment.
Bibliographic record
Abstract
OBJECTIVE: To summarize the pathophysiology, epidemiology, screening, diagnosis, and treatment of familial hypercholesterolemia (FH). QUALITY OF EVIDENCE: A PubMed search was conducted (inception to July 2014) for articles on pathophysiology, screening, diagnosis, and management of FH, supplemented with hand searches of bibliographies of guidelines and reviews. A supporting level of evidence for each recommendation was categorized as level I (randomized controlled trial or systematic review of randomized controlled trials), level II (observational study), or level III (expert opinion). The best available evidence is mostly level II or III. MAIN MESSAGE: Familial hypercholesterolemia affects 1 in 500 Canadians. Risk of a coronary event is high in these patients and is underestimated by risk calculators (eg, Framingham). Clinicians should screen patients according to guidelines and suspect FH in any patient with a premature cardiovascular event, physical stigmata of hypercholesterolemia, or an elevated plasma lipid level. Physicians should diagnose FH using either the Simon Broome or Dutch Lipid Network criteria. Management of heterozygous FH includes reducing low-density lipoprotein levels by 50% or more from baseline with high-dose statins and other lipid-lowering agents. Clinicians should refer any patient with homozygous FH to a specialized centre. CONCLUSION: Familial hypercholesterolemia represents an important cause of premature cardiovascular disease in Canadians. Early identification and aggressive treatment of individuals with FH reduces cardiovascular morbidity and mortality.
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 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".