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Record W4383683878 · doi:10.58931/cdet.2023.119

Addressing NAFLD as a type 2 diabetes complication using the emerging paradigms in diagnostic and management techniques

2023· article· en· W4383683878 on OpenAlexaffabout
Harpreet S. Bajaj

Bibliographic record

VenueCanadian Diabetes & Endocrinology Today · 2023
Typearticle
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsLMC Diabetes & Endocrinology (Canada)
Fundersnot available
KeywordsMedicineFatty liverCirrhosisLiver biopsyGastroenterologyType 2 diabetesInternal medicineLiver diseaseDiabetes mellitusComplicationHepatocellular carcinomaBody mass indexType 2 Diabetes MellitusChronic liver diseaseDiseaseBiopsyEndocrinology

Abstract

fetched live from OpenAlex

Several critical epidemiological facts underscore the urgent need to address non-alcoholic fatty liver disease (NAFLD) in type 2 diabetes (T2D):
 
 NAFLD is the most common liver disease in Canada, affecting approximately one in four Canadians;
 NAFLD is projected to become the number one leading indication for liver transplant by 2025;
 Individuals with T2D are at the greatest risk of liver disease progression in NAFLD; T2D is the main predictor of NAFLD-related liver fibrosis and mortality.
 
 To put this into clinical perspective, consider the following fictitious case: A 45-year-old teetotaler, Caucasian woman with T2D and a body mass index (BMI) of 32 kg/m2, with no microvascular or macrovascular complications, was incidentally found to have “fatty liver” on abdominal ultrasound. ALT and AST were both within normal range. She was recommended to lose weight and control A1C. Twelve years later, she developed hematemesis and liver biopsy confirmed end-stage liver cirrhosis, with hepatocellular carcinoma. She was scheduled to undergo a liver transplant at age 59.
 Despite the three established facts presented above and an abundance of cases similar to the one presented here, currently NAFLD is not being addressed during routine diabetes care as a complication of T2D.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.673

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.044
GPT teacher head0.315
Teacher spread0.271 · 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
Published2023
Admission routes2
Has abstractyes

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