Treatment of Hypernatremia in Breastfeeding Neonates: A Systematic Review
Bibliographic record
Abstract
BACKGROUND/AIMS: Hypernatremic dehydration in term neonates is associated with inadequate fluid intake, usually related to insufficient lactation. The use of hypotonic fluids is appropriate to dilute serum sodium (SNa), but cerebral edema may develop when it happens abruptly. Our objective was to clarify how to correct hypernatremic dehydration properly. METHODS: The following databases were searched, limited to studies published until January 31st, 2016: Clinical Trials, MEDLINE/PubMed, EMBASE, LILACS, and the Cochrane Library. We included open-label trials, nonrandomized controlled trials, or prospective and retrospective case series evaluating relevant outcomes. Information regarding the way of administering the treatment, type of fluid used, rates of complications and outcomes, as well as the rate of SNa reduction were collected. RESULTS: Searches yielded 771 articles: 64 had the full text reviewed and 9 were included. No randomized clinical trials or systematic reviews focusing on treatment of hypernatremic dehydration and its outcomes were found. We found a scarcity of high quality studies and great methodology heterogeneity. CONCLUSIONS: More severe hypernatremia is at greater risk of causing severe adverse effects of treatment. There is no consensus about the optimal rate of SNa drop in this population, but a slower correction appears to be safer. Questions as when parenteral fluids are indicated remain unanswered.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".