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Record W4384338506 · doi:10.3390/children10071220

Diagnosis and Management of Neonatal Hypoglycemia: A Comprehensive Review of Guidelines

2023· review· en· W4384338506 on OpenAlexaboutno aff
Sonia Giouleka, Maria Gkiouleka, Ιoannis Tsakiridis, Anastasia Daniilidou, Apostolos Mamopoulos, Apostolos Athanasiadis, Themistoklis Dagklis

Bibliographic record

VenueChildren · 2023
Typereview
Languageen
FieldMedicine
TopicHyperglycemia and glycemic control in critically ill and hospitalized patients
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHypoglycemiaPediatricsIntensive care medicineNewborn screeningAsymptomaticNeonatal hypoglycemiaGuidelinePregnancySurgeryInternal medicinePathologyGestation

Abstract

fetched live from OpenAlex

Hypoglycemia represents one of the most frequent metabolic disturbances of the neonate, associated with increased morbidity and mortality, especially if left untreated or diagnosed after the establishment of brain damage. The aim of this study was to review and compare the recommendations from the most recently published influential guidelines on the diagnosis, screening, prevention and management of this common neonatal complication. Therefore, a descriptive review of the guidelines from the American Academy of Pediatrics (AAP), the British Association of Perinatal Medicine (BAPM), the European Foundation for the Care of the Newborn Infants (EFCNI), the Queensland Clinical Guidelines-Australia (AUS), the Canadian Pediatric Society (CPS) and the Pediatric Endocrine Society (PES) on neonatal hypoglycemia was carried out. There is a consensus among the reviewed guidelines on the risk factors, the clinical signs and symptoms of NH, and the main preventive strategies. Additionally, the importance of early recognition of at-risk infants, timely identification of NH and prompt initiation of treatment in optimizing the outcomes of hypoglycemic neonates are universally highlighted. All medical societies, except PES, recommend screening for NH in asymptomatic high-risk and symptomatic newborn infants, but they do not provide consistent screening approaches. Moreover, the reviewed guidelines point out that the diagnosis of NH should be confirmed by laboratory methods of BGL measurement, although treatment should not be delayed until the results become available. The definition of NH lacks uniformity and it is generally agreed that a single BG value cannot accurately define this clinical entity. Therefore, all medical societies support the use of operational thresholds for the management of NH, although discrepancies exist regarding the recommended cut-off values, the optimal treatment and surveillance strategies of both symptomatic and asymptomatic hypoglycemic neonates as well as the treatment targets. Over the past several decades, ΝH has remained an issue of keen debate as it is a preventable cause of brain injury and neurodevelopmental impairment; however, there is no clear definition or consistent treatment policies. Thus, the establishment of specific diagnostic criteria and uniform protocols for the management of this common biochemical disorder is of paramount importance as it will hopefully allow for the early identification of infants at risk, the establishment of efficient preventive measures, the optimal treatment in the first hours of a neonate's life and, subsequently, the improvement of neonatal outcomes.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0080.005
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.111
GPT teacher head0.406
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations38
Published2023
Admission routes1
Has abstractyes

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