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Record W2890913910 · doi:10.7759/cureus.3292

Carglumic Acid Treatment of a Patient with Recurrent Valproic Acid-induced Hyperammonemia: A Rare Case Report

2018· article· en· W2890913910 on OpenAlexaff
Yasar Sattar, Saad Wasiq, Waqas Yasin, Ali Khan, Mahwish Adnan, Shristi Shrestha, Nirav Patel, Sharaad Latchana

Bibliographic record

VenueCureus · 2018
Typearticle
Languageen
FieldMedicine
TopicPharmacological Effects and Toxicity Studies
Canadian institutionsCentre for Addiction and Mental Health
Fundersnot available
KeywordsHyperammonemiaValproic AcidMedicineLethargyAnticonvulsantUrea cycleInternal medicineEpilepsyPharmacologyGastroenterologyPsychiatryBiochemistry

Abstract

fetched live from OpenAlex

Valproic acid, first manufactured as an anticonvulsant, is commonly used to treat both neurological and psychiatric conditions. A rare and deadly side effect of this medication is hyperammonemia, presenting as lethargy, confusion, seizure, and, ultimately, coma. In rare circumstances, hyperammonemia can be recurrent and devastating, especially in patients with an underlying N-acetyl glutamate synthase (NAGS) deficiency, as the valproic acid can enhance this enzyme deficiency and inhibit the conversion of ammonia into urea in the liver. For these subtypes of patients, the United States Food and Drug Administration (US FDA) has recently approved carglumic acid, a medication that can act as a scavenger by effectively increasing the levels of NAGS, ultimately enhancing the conversion of ammonia to urea. In our case report, we have mentioned a patient with treatment-resistant bipolar disorder, who presented with elevated ammonia levels secondary to valproic acid treatment. Valproic acid was the only drug that was effective in his case, so we initiated therapy to reduce his elevated ammonia levels. After a thorough evaluation, we found the patient had a genetic NAGS deficiency. Carglumic acid was initiated and proved efficacious in our patient.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.354
Threshold uncertainty score0.538

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.042
GPT teacher head0.330
Teacher spread0.288 · 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 designBench or experimental
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

Citations9
Published2018
Admission routes1
Has abstractyes

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