A Personalized Medicine Approach is Best for Patients with Homozygous Familial Hypercholesterolemia
Bibliographic record
Abstract
Homozygous familial hypercholesterolemia (HoFH) is an autosomal semi-dominant condition characterized by biallelic pathogenic variants impacting low-density lipoprotein receptor (LDLR) function. Affected individuals have severely elevated LDL cholesterol, early onset atherosclerotic heart disease and/or aortic stenosis, and characteristic clinical findings. While the cause is known and diagnosis is relatively simple, real-world HoFH care presents many complexities, including genetic heterogeneity and the diverse personal and social circumstances that influence care. Genetics-informed treatment involves a trial-and-error approach that warrants specific considerations during pregnancy. Thus, HoFH care requires a deep understanding of personal factors, social determinants of health, and a flexible, adaptable approach to treatment, all of which justify the need for personalized care. Framed by complexity theory, this review offers strategies for personalizing HoFH care, including a reconceptualization of the definition of health and implementing a multidisciplinary team approach. We also recommend integrating complexity theory and systems thinking into clinical care. By doing so, we illustrate the advantages of classifying knowledge complexity to inform clinical decision-making. We also demonstrate how openness to relationship-building and time investment is critical to materializing personalized care to HoFH.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".