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Record W3024535437 · doi:10.22374/cjgim.v14i4.339

Minding The Gap: Severe Anion Gap Metabolic Acidosis Associated With 5-Oxoproline Secondary To Chronic Acetaminophen Use

2019· article· en· W3024535437 on OpenAlexaffvenue
Claudia Frankfurter, Kevin Venus, David Frost

Bibliographic record

VenueCanadian Journal of General Internal Medicine · 2019
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicDrug-Induced Hepatotoxicity and Protection
Canadian institutionsToronto Western HospitalUniversity of Toronto
Fundersnot available
KeywordsAnion gapAcetaminophenMetabolic acidosisMedicineInternal medicineAcidosisGastroenterologyAbdominal painAnesthesia

Abstract

fetched live from OpenAlex

An 89-year-old man with multiple comorbidities presented to the emergency department with diffuse abdominal pain and dyspnea. He was found to have a severe anion-gap metabolic acidosis with the normal osmolar gap. An initial panel of investigations for common causes of anion-gap metabolic acidosis was unremarkable. Further history revealed long-term daily acetaminophen use. A presumptive diagnosis of 5-oxoprolinemia secondary to chronic acetaminophen use was made. Despite supportive care, the patient did not survive. There is emerging literature on elevated anion gap metabolic acidosis induced by the accumulation of 5-oxoproline, an intermediate organic acid in the gamma-glutamyl cycle. A quantitative profile of urinary organic acids to measure 5-oxoproline is valuable in confirming the diagnosis. Treatment is largely supportive, consisting of cessation of acetaminophen, alkali therapy, and N-acetylcysteine. Clinicians should consider 5-oxoprolemia in patients who present with an otherwise unexplained anion gap metabolic acidosis and a history of chronic acetaminophen use.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.659
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.112
GPT teacher head0.366
Teacher spread0.254 · 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.

Study designNot applicable
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

Citations1
Published2019
Admission routes2
Has abstractyes

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