A Personalized Medicine Approach is Best for Patients with Homozygous Familial Hypercholesterolemia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it