Management and Nutrition of Neonates during the COVID-19 Pandemic: A Review of the Existing Guidelines and Recommendations
Bibliographic record
Abstract
OBJECTIVE: We aimed at reviewing the currently available guidelines and scientific recommendations regarding the neonatal in-hospital management and feeding in the light of the coronavirus disease 2019 (COVID-19) pandemic. STUDY DESIGN: We systematically searched the guideline databases, Medline, Embase, and nationale/international neonatal societies websites as of June 19, 2020, for guidelines on neonatal management and feeding during the COVID-19 pandemic, at the same time assessing the methodological quality using the Appraisal of Guidelines for Research and Evaluation II tool. RESULTS: Eleven guidelines were included. The Chinese and American recommendations suggest separation of the mother and her neonate, whereas in French, Italian, UK, Canadian, and World Health Organization consensus documents the rooming-in is suggested, with Centers for Disease Control and Prevention guidelines suggesting to decide on a case-by-case basis. All the guidelines recommend breastfeeding or feeding with expressed maternal milk; the only exception is the Chinese recommendations, these last suggesting to avoid breastfeeding. CONCLUSION: This review may provide a useful tool for clinicians and organizers, highlighting differences and similarities of the existing guidelines on the management and feeding strategies in the light of the COVID-19 pandemic. KEY POINTS: · This study compares guidelines on management and nutrition of a newborn born to a mother with SARS-CoV-2 infection.. · Existing guidelines on neonatal management and nutrition during the SARS-CoV-2 pandemic show many differences.. · The majority of recommendations are mainly based on experts' opinion and are not evidence-based..
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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 | 0.013 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".