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Record W3180228558 · doi:10.18433/jpps32116

Treatment of Hypertriglyceridemia with Aggressive Continuous Intravenous Insulin

2021· article· en· W3180228558 on OpenAlexvenueno aff
Abigail Hoff, Kara Piechowski

Bibliographic record

VenueJournal of Pharmacy & Pharmaceutical Sciences · 2021
Typearticle
Languageen
FieldMedicine
TopicPancreatitis Pathology and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsHypertriglyceridemiaMedicineInterquartile rangeInsulinTriglycerideAcute pancreatitisInternal medicineGastroenterologyAnesthesiaSurgeryCholesterol

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.722
Threshold uncertainty score0.474

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.056
GPT teacher head0.387
Teacher spread0.331 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Pharmacy & Pharmaceutical SciencesSame topicPancreatitis Pathology and TreatmentFrench-language works237,207