Familial Hyperlipidemia Screening, Treatment and New Considerations
Bibliographic record
Abstract
We present a case of a 47-year-old male who came to the endocrinology department at the request of his GP. He was noted to have elevated lipid levels, twice the normal range. Genetic testing was performed which showed he was heterozygous for familial hypercholesterolemia (FH). His three children subsequently had their lipid levels tested. They all had elevated lipids (cholesterol and LDL) and tested positive for FH. This case highlights the importance of lipid screening in children as the prevalence of FH is 1 in 200, which is higher than previously thought. This has led to the underdiagnosis of FH in both the pediatric and adult population. Often on initial presentation of the disease, irreversible damage has been done increasing the patients’ morbidity and mortality. With newer drugs being introduced and the persistent push by the National Heart, Lung and Blood Institute Expert Panel towards initiating a child screening protocol, there is hope that this disease will be manageable and less detrimental to those affected. J Med Cases. 2016;7(7):258-259 doi: https://doi.org/10.14740/jmc2533w
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".