Infant Parenteral Nutrition Remains a Significant Source for Aluminum Toxicity
Bibliographic record
Abstract
BACKGROUND: Aluminum toxicity is associated with anemia, impaired bone metabolism, neurologic defects, and parenteral nutrition (PN)-associated liver disease. This element is a ubiquitous contaminant of PN components, especially in infant formulations. We assessed the current levels of aluminum contamination in infant PN at a level III neonatal intensive care unit. MATERIALS AND METHODS: Thirty samples of PN prepared in the same hospital for infants aged <30 days (mean [SD] weight, 1.54 [0.71] kg) were collected from discarded solution. Each sample was analyzed for aluminum content via inductively coupled plasma mass spectrometry. The components of PN (from label) and measured aluminum content were then compared using linear regression and 1-way analysis of variance. RESULTS: The mean (SD) aluminum contamination of infant PN was 14.02 (6.51) mcg/kg/d. Only 3 samples were <5 mcg/kg/d. Aluminum levels and infant weight were not associated. Linear regression revealed a significant correlation between aluminum and both calcium gluconate ( P < .0001) and phosphate ( P = .05), with a trend between aluminum and potassium ( P = .07). CONCLUSIONS: Aluminum contamination in infant PN remains almost 3 times higher than the advised maximum exposure (<5 mcg/kg/d, Food and Drug Administration 2004). Unexpectedly, an association between infant weight and aluminum exposure was not apparent, likely due to the homogeneity of our population. Isolating the source of aluminum contamination is difficult, as multiple components appear to be involved. Calcium gluconate is likely still a major contributor, but further investigations into individual components are warranted to promote the reduction of aluminum in infant PN.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".