Monitoring and management of hypertriglyceridemia in extremely low birth weight neonates receiving intravenous lipid emulsions: A national survey
Bibliographic record
Abstract
AIM: To assess the practice variation of defining, monitoring and managing hypertriglyceridemia (HTG) in extremely low birth weight neonates receiving intravenous lipid emulsions (IVLE). METHODS: An 8-question survey created via the web survey site Qualtrics was distributed to neonatologists, neonatal nurse practitioners and fellows within the Section of Neonatal-Perinatal Medicine email directory list in the United States and Canada. Survey results were obtained between August and September 2022. RESULTS: There were 249 respondents from approximately 4000 members within the Section of Neonatal-Perinatal Medicine. Responses were documented as a frequency (percentage) with a margin of error of plus or minus 6.2 %. Most respondents were neonatologists, individuals practicing for >10 years and reported a unit-based policy for IVLE initiation and advancement. The definitions of HTG varied among respondents, with the majority (42.7 %) reporting a defining threshold of >200 mg/dL. Nineteen percent of respondents reported not routinely monitoring serum triglyceride concentrations with variable triglyceride monitoring intervals reported by other survey respondents. Regarding elevated triglyceride concentrations, 19.0 % reported decreasing the IVLE rate and checking triglyceride concentrations until normalization; 14.6 % reported IVLE discontinuation and monitoring triglyceride concentrations until normalization; 61.9 % reported using a combination of the above practices; and 4.4 % reported individualized practices for IVLE management with elevated triglyceride concentrations. CONCLUSION: This survey demonstrates a high variation in defining, monitoring and managing HTG in extremely low birth weight neonates and emphasizes the need for studies to better guide this practice.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".