Low plasma magnesium is associated with impaired brain metabolism in neonates with hypoxic‐ischaemic encephalopathy
Bibliographic record
Abstract
AIM: To determine the association between lowest plasma magnesium concentration and brain metabolism, and whether magnetic resonance imaging brain injury patterns moderated the association in hypoxic-ischemic encephalopathy. METHODS: In 131 early (day-of-life 3) and 65 late (day-of-life 10) scans of term encephalopathic infants born between 2004 and 2012, we examined the association of lowest plasma magnesium (until day-of-life 3) on basal ganglia and white matter peak metabolite ratios on magnetic resonance spectroscopy independent of covariates, stratified by the predominant patterns of injury (normal, basal nuclei/total, watershed, multifocal) using multiple linear regression. RESULTS: Lowest plasma magnesium was associated with lower white matter N-acetyl-aspartate/choline in the multifocal pattern on early scan (regression-coefficient, β: 0.13; 95% CI: 0.04, 0.22) and in the basal nuclei/total pattern on late scan (β: 0.08; 95% CI: 0.02, 0.15), and was negatively associated with basal ganglia lactate/N-acetyl-aspartate (β: -0.16; 95% CI: -0.05, -0.28) and lactate/choline (β: -0.1; 95% CI: -0.03, -0.17) ratio in the basal nuclei/total pattern on late scan independent of hypomagnesaemia correction, cooling and postmenstrual age at scan. Lowest plasma magnesium was not associated with metabolite ratios in other brain injury patterns. CONCLUSION: In infants with hypoxic-ischaemic encephalopathy, predominant patterns of brain injury moderated the association between lowest plasma magnesium in the first three days of life and impaired brain metabolism.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".