Amino acids for the prevention of mortality and morbidity in preterm infants: a systematic review and network meta-analysis
Bibliographic record
Abstract
To determine the effectiveness and safety of amino acids in preventing the mortality and morbidity among preterm infants. We conducted a systematic review and network meta-analysis. We searched MEDLINE, EMBASE, Web of Science, CINAHL, Scopus, Cochrane, and Google Scholar, and grey literature, from databases inception to January 2021. We included randomized trials that evaluated any amino acids on preterm or low-birth weight infants. We performed frequentist pairwise and network meta-analyses and used the GRADE methodology to assess the certainty of the evidence and provide a summary of the results.We included 18 trials (3702 infants). Low certainty evidence showed that there seems to be no benefit for arginine, glutamine, or N-acetylcysteine in reducing all-cause mortality. Oral arginine likely results in reduction of necrotizin enterocolitis (NEC) stage ≥ II (OR 0.48; 95% CI 0.26-0.90; moderate certainty). Oral glutamine may reduce the likelihood of developing late-onset sepsis (LOS) compared to placebo (OR 0.62; 95% CI 0.47-0.82; low certainty); and likely reduces time to reach full enteral feeding (MD = - 2.63 days; 95% CI - 4.99 to - 0.27; moderate certainty). Amino acids may have no effect on mortality. Oral arginine may reduce severe NEC, and oral glutamine may reduce LOS and the time to reach full feeding.Systematic review registration: PROSPERO registration number: CRD4201603873.
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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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.039 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".