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Record W2590654910 · doi:10.14740/gr778w

Relationship Between Obesity and Liver Enzymes Levels in Turner’s Syndrome

2017· article· en· W2590654910 on OpenAlexvenueno aff
Farzaneh Rohani, Fatemeh Golgiri, Mohammad Reza Alaii, Mojgan Karimi, Parham Nikraftar, Ramin Bozorgmehr

Bibliographic record

VenueGastroenterology Research · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and Clinical Aspects of Sex Determination and Chromosomal Abnormalities
Canadian institutionsnot available
Fundersnot available
KeywordsTurner syndromeLiver enzymeMedicineObesityEnzymeEndocrinologyInternal medicineBiochemistryBiology

Abstract

fetched live from OpenAlex

BACKGROUND: Liver enzyme abnormalities have been reported in Turner's syndrome (TS). There are some studies about possible causes of abnormal levels of liver enzymes. One of the main suggestions is obesity. The study aimed to determine the relationship between obesity and liver enzymes levels in patients with TS. METHODS: Forty-one karyotype-proven TS patients referred to Endocrinology and Metabolism Research Center were included in this cross-sectional study. Height and weight of patients were measured and their body mass index (BMI) was calculated. The patients were divided into two groups as the control group including 27 cases (65.8%) with normal BMI (defined as < 85th percentile for age and gender), and the overweight group including 14 cases (34.2%) (defined as BMI > 85th percentile for age and gender). Serum levels of aspartate transaminase (AST), alanine transaminase (ALT) and alkaline phosphatase (AlkPh) were measured. RESULTS: There were no statistically significant differences regarding AST (27 ± 2.7 vs. 29.6 ± 5.85 U/L; P = 0.3), ALT (20.1 ± 2.45 vs. 22.2 ± 5.85 U/L; P = 0.5), and AlkPh (583.4 ± 2.45 vs. 472.8 ± 161.5 U/L; P = 0.28) between overweight TS patients and those with normal BMI. CONCLUSION: There was no significant difference in liver enzyme levels between TS patients with normal BMI and those who were overweight.

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)0.001
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 score0.002
Threshold uncertainty score0.357

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.110
GPT teacher head0.378
Teacher spread0.268 · 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

Citations3
Published2017
Admission routes1
Has abstractyes

Explore more

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