Use of Lifestyle Modifications for Management of a Patient with Severely High Total Cholesterol (> 14 mmol/L) and Triglycerides (> 40 mmol/L)
Bibliographic record
Abstract
In this report, we describe a case of a 37-year old man who presented with a history of total cholesterol > 14 mmol/L and triglyceride levels > 40 mmol/L. The patient was initially thought to have familial hypercholesterolemia due to his elevated total cholesterol, by his family physician. He was prescribed evolucumab, a proprotein convertase subtilisin/ kexin type 9 inhibitor drug which has shown efficacy for lowering low-density lipoprotein-cholesterol levels, to reduce his high total cholesterol. However, in this patient, the elevated total cholesterol was likely due to hypertriglyceridemia, rather than increased low-density lipoprotein-cholesterol levels. Through this case we provide an approach for the clinical management of patients with elevated total cholesterol with underlying triglycerides ≥ 10 mmol/L. The primary intervention for management of triglycerides ≥ 10 mmol/L involves lifestyle modifications including, changes in diet, exercise, reduction in body mass index, and abstinence from alcohol consumption. Secondary intervention involves management through pharmacotherapy with fibrates and statins. Creating a plan of action with the patient, incorporating lifestyle modifications alone, the patient was able to reduce the triglycerides from an average of 44.94 mmol/L to 3.28 mmol/L.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".