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Record W4229885678 · doi:10.14740/jem326w

Inaccuracies of Hemoglobin A1c in Liver Cirrhosis: A Case Report

2016· article· en· W4229885678 on OpenAlexvenueno aff
Mathew Clarke, Jamila Benmoussa, Amulya Penmetsa, Philip Otterbeck, Farhang Ebrahimi, Jay Nfonoyim

Bibliographic record

VenueJournal of Endocrinology and Metabolism · 2016
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCirrhosisFructosamineGlycemicAscitesGastroenterologyInternal medicineDiabetes mellitusLiver diseaseAnemiaGlycated hemoglobinChronic liver diseaseType 2 diabetesEndocrinologyInsulin

Abstract

fetched live from OpenAlex

Hemoglobin A1c (HbA1c) is the gold standard for the measurement of long-range glycemic control in patients with diabetes mellitus type 2 (T2DM). In a rare subset of patients, this measurement may not be reliable. Inaccuracies of HbA1c in liver cirrhosis are rare, but documented. The objective of this study was to increase awareness about low HbA1c in liver cirrhosis and discuss alternative biomarkers that can be used to measure glycemic control. We present the case of a 61-year-old Caucasian female, with history of hepatitis C and uncontrolled T2DM, who was admitted for evaluation of compensated liver cirrhosis. She was found to have blood glucoses greater than 500 mg/dL; however, her HbA1c was measured at 5.5%. Ultrasound of the abdomen showed liver cirrhosis, ascites, and splenomegaly. Blood work revealed acute kidney injury, anemia of chronic disease, normal albumin level, and low HbA1c. Fructosamine and glycated albumin were high, indicating a hyperglycemic status during the last 3 weeks. HbA1c can be falsely low in liver cirrhosis, and can give a false assumption about control of the diabetic disease process. In this case, other biomarkers can be used to monitor glycemic control; by far frequent finger stick monitoring is the best method. J Endocrinol Metab. 2016;6(1):30-32 doi: http://dx.doi.org/10.14740/jem326w

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.458
Threshold uncertainty score0.184

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.027
GPT teacher head0.310
Teacher spread0.283 · 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

Citations8
Published2016
Admission routes1
Has abstractyes

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