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Record W4386192177 · doi:10.14740/gr1634

Coma With Hyperammonemia in a Patient With Kwashiorkor

2023· article· en· W4386192177 on OpenAlexvenueno aff
Thibault Vieille, François Feillet, Arnaud Wiedemann, Hadrien Winiszewski, Gaël Piton

Bibliographic record

VenueGastroenterology Research · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism and Genetic Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsHyperammonemiaMedicineUrea cycleMalnutritionComa (optics)Internal medicineContext (archaeology)Cerebral edemaGastroenterologyHepatic encephalopathyEndocrinologyMetabolic disorderPediatricsCirrhosisBiochemistryAmino acidChemistryBiology

Abstract

fetched live from OpenAlex

We describe a case of coma-related hyperammonemia in a woman presenting with severe edematous malnutrition (Kwashiorkor-like), without underlying hepatic disease. Our main hypothesis is that the patient developed a functional urea cycle disorder, due to the inability to synthesize N-acetylglutamate which is the activator of the first enzymes (carbamoyl phosphate synthetase) of urea cycle, in a context of severe deficiency of essential amino acids and of acetyl-CoA. Severe hyperammonemia is a medical emergency exposing to the risk of cerebral edema. Urgent treatment should interrupt protein intake, stimulate protein anabolism, and remove ammonia from the blood using renal replacement therapy and ammonia scavengers. Hyperammonemia should be searched in case of unexplained coma, even among patients without hepatic disorder, in particular among young patients. Hyperammonemia should also be searched among patients with severe protein-calorie malnutrition.

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)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.021
GPT teacher head0.299
Teacher spread0.278 · 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 designCase report
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

Citations2
Published2023
Admission routes1
Has abstractyes

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