In preterm infants, does the supplementation of carnitine to parenteral nutrition improve the following clinical outcomes: Growth, lipid metabolism and apneic spells?
Bibliographic record
Abstract
Carnitine is found in human milk (1) and is absent in total parenteral nutrition (TPN) (2). Preterm infants have lower tissue carnitine stores than term infants (3,4), and levels drop quickly within the first two weeks of life if fed diets lacking carnitine (5,6). Because of the role of carnitine in fatty acid oxidation (which contributes to energy metabolism and growth), there has been considerable interest in carnitine during the neonatal period (2). There are at least two theoretical reasons for adding carnitine to neonatal diets: nutritional biochemistry and simulation of human milk composition. It is for these two reasons, and not for clinical reasons, that carnitine is added to infant formula in concentrations similar to those found in human milk (7). As with enterally fed infants, there is no clinical advantage of adding carnitine to TPN of intravenously fed infants, as noted in the previous data analysis (Part A, pages 571–572). However, all studies were small and mostly short-term. There are no studies on carnitine supplementation in infants with short bowel syndrome or infants receiving home TPN who may need intravenous feeding for years and who may have the highest risk of developing functional carnitine deficiency. Interestingly, while there is insufficient clinical data to recommend supplementation of TPN with carnitine for preterm infants, carnitine is being added to infant formulas without the support of evidence-based clinical data or without firm recommendations by the Canadian Paediatric Society or Health Canada (7,8). Considering the evidence available, there is no clinical advantage in adding carnitine to short-term regimens of TPN of newborn infants. Whether or not carnitine supplementation offers an advantage to infants receiving long-term parenteral nutrition remains to be researched.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".