Standardised neonatal parenteral nutrition formulations – Australasian Neonatal Parenteral Nutrition Consensus update 2017
Bibliographic record
Abstract
Abstract Background: The first consensus standardised neonatal parenteral nutrition formulations were implemented in many neonatal units in Australia in 2012. The current update involving 49 units from Australia, New Zealand, Singapore, Malaysia and India was conducted between September 2015 and December 2017 with the aim to review and update the 2012 formulations and guidelines. Methods: A systematic review of available evidence for each parenteral nutrient was undertaken and new standardised formulations and guidelines were developed. Results: Five existing preterm Amino acid-Dextrose formulations have been modified and two new concentrated Amino acid-Dextrose formulations added to optimise amino acid and nutrient intake according to gestation. Organic phosphate has replaced inorganic phosphate allowing for an increase in calcium and phosphate content, and acetate reduced. Lipid emulsions are unchanged, with both SMOFlipid (Fresenius Kabi, Australia) and ClinOleic (Baxter Healthcare, Australia) preparations included. The physicochemical compatibility and stability of all formulations have been tested and confirmed. Guidelines to standardise the parenteral nutrition clinical practice across facilities have also been developed. Conclusions: The 2017 PN formulations and guidelines developed by the 2017 Neonatal Parenteral Nutrition Consensus Group offer concise and practical instructions to clinicians on how to implement current and up-to-date evidence based PN to the NICU population.
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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 | 0.037 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".