Monitoring and management of hypertriglyceridemia in extremely low birth weight neonates receiving intravenous lipid emulsions: A national survey
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it