Treatment of Hypertriglyceridemia with Aggressive Continuous Intravenous Insulin
Bibliographic record
Abstract
PURPOSE: Severe hypertriglyceridemia requiring hospitalization for intravenous insulin to lower triglycerides and prevent complications of pancreatitis is becoming an increasing problem with little consensus treatment evidence. This is the largest case series to date to evaluate this under-studied area of literature. The objective of this study was to determine the average time to triglyceride lowering less than 500 mg/dL. METHODS: This was a retrospective case series from March 2018 to March 2020 at a single rural academic medical center. 23 patients were included who received weight-based intravenous insulin at 0.1 units/kg/hour through a hypertriglyceridemia management order-set over a two-year period. RESULTS: The median triglyceride level at initiation of the insulin infusion was 3759 mg/dL with an interquartile range of 5555. The median time to a triglyceride level less than 1000 mg/dL and 500 mg/dL was 45 hours (1.8 days) and 75 hours (3.1 days) respectively. Patients remained on intravenous insulin for a median of 60 hours (2.5 days). CONCLUSIONS: In this largest case series to date evaluating the use of intravenous insulin for the treatment of hypertriglyceridemia, a weight-based insulin infusion demonstrated reduction of triglyceride levels to less than 1000 mg/dL in approximately 2 days and less than 500 mg/dL in approximately 3 days.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".