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Record W2793074980 · doi:10.15386/cjmed-993

NON-ALCOHOLIC FATTY LIVER DISEASE: AN UPDATE ON DIAGNOSIS

2018· review· en· W2793074980 on OpenAlexaff
Dan L. Dumitraşcu, Manuela G. Neuman

Bibliographic record

VenueMedicine and Pharmacy Reports · 2018
Typereview
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsUniversity of TorontoPetro-Canada
Fundersnot available
KeywordsFatty liverAlcoholic liver diseaseDiseaseMedicineGastroenterologyInternal medicineCirrhosis

Abstract

fetched live from OpenAlex

BACKGROUND AND AIM: The non-alcoholic fatty liver disease (NAFLD) and its sub-entity, the non-alcoholic steatohepatitis (NASH) represent a field of a tremendous progress in recent years. Clinicians need to remain updated with new data on pathogenesis and therapy. The present mini review aims to present some new scientific reports on the diagnosis of NAFLD and NASH for clinical practitioners. METHODS: A systematic literature search of the main international databases was performed. We looked for seminal and innovative papers published in main international languages. A narrative review of the topic was consequently written. RESULTS: This review describes new data on the diagnosis of NAFLD including NASH. Liver punction biopsy remains the gold standard. However many patients and clinicians prefer to use noninvasive methods. We present the serological tests and the imaging methods used to diagnose inflammation and fibrosis occurring in NAFLD and NASH. CONCLUSIONS: NAFLD-NASH are multifaceted entities that have to be diagnosed and treated by skilled and informed practitioners.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.858
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
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)0.0020.000

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 head0.132
GPT teacher head0.430
Teacher spread0.298 · 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
GenreReview

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

Citations57
Published2018
Admission routes1
Has abstractyes

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