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Record W4415005385 · doi:10.3138/jcin-2025-0004

Profound neonatal encephalopathy in a neonate with iatrogenic hypermagnesemia

2025· article· en· W4415005385 on OpenAlexaff
Emily Lo, Clara Gonzalez Lopez, Aoife Hurley, Hemasaree Kandraju, Tamorah Lewis, Mehmet Nevzat Çizmeci, Estelle B. Gauda

Bibliographic record

VenueJournal of Clinical Insights in Neonatology · 2025
Typearticle
Languageen
FieldNursing
TopicMagnesium in Health and Disease
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsHypermagnesemiaComa (optics)EncephalopathyDifferential diagnosisResuscitationSeptic shockShock (circulatory)Neonatal intensive care unitNeurointensive care

Abstract

fetched live from OpenAlex

Introduction: Neonatal encephalopathy has a broad differential diagnosis, including hypoxic-ischemic encephalopathy, in-born errors of metabolism, intracranial hemorrhage, genetic conditions, and severe septic shock. Case Presentation: We present a case of a preterm patient with coma and shock on day 3 of life after an unremarkable course in the first 72 hours of life. Clinical investigations performed on her admission to a tertiary neonatal intensive care unit showed severe hypermagnesemia that required fluid resuscitation and aggressive diuresis for treatment. As her blood magnesium levels decreased, her neurological exam improved, with a completely normal neurological exam by day 7 of life. Root cause analysis revealed that the total parenteral nutrition was the source of iatrogenic hypermagnesemia in this infant. Conclusion: This case highlights the importance of including hypermagnesemia in the differential diagnosis of neonatal encephalopathy of an infant presenting with systemic signs such as coma, apnea, bradycardia, and refractory hypotension. Early recognition of medication overdose and systemic toxicity in a preterm infant with neurological dysfunction is essential for timely treatment and prevention of further harm.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score0.835

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.385
Teacher spread0.362 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

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