Acute Pancreatitis Secondary to Severe Hypertriglyceridemia: Management of Severe Hypertriglyceridemia in Emergency Setting
Bibliographic record
Abstract
Hypertriglyceridemia (HTG) is the third most common cause of acute pancreatitis (AP). The incidence of AP is around 10-20% with levels > 2,000 mg/dL. We present here a case of a 44-year-old male with history of uncontrolled diabetes mellitus and HTG admitted with severe abdominal pain. Labs revealed elevated lipase and amylase. CT of abdomen with contrast showed AP. He was found to have a triglyceride (TG) level of 6,672 mg/dL. Besides conventional treatment for AP with intravenous (IV) hydration, he was started on IV regular insulin along with dextrose saline. He had marked improvement in his TG level the next day. He was continued on insulin and dextrose saline with hourly glucose monitoring until TG was < 500 mg/dL. He was discharged on statins and fenofibrate. The goal of management of AP secondary to severe HTG in emergency setting is to lower the TG levels to less than 500 as quickly as possible as lower levels are associated with good clinical outcomes. Apheresis and IV insulin are both helpful in lowering TG levels with no randomized controlled trials showing greater efficacy of one over other. Further episodes of AP can be prevented by lifestyle modification and lipid lowering drugs to keep TG levels below 500 mg/dL. Fibrates are first-line drugs to lower TG and used either alone or in conjunction with statins. Periodic plasmapheresis can also be considered in some non-compliant patients with recurrent episodes of pancreatitis.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".