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Record W4318355268 · doi:10.53350/pjmhs20221611758

Low Serum Testosterone Level and Its Relationship with Hypogonadism in Patients with Chronic Liver Disease

2022· article· en· W4318355268 on OpenAlexaff
Waqar Zafar, Farah Naz Tahir, Hooria Bakhtawar, Muhammad Abdullah, Mahboob Qadir, Misbah Hanif

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicHormonal and reproductive studies
Canadian institutionsAbbott (Canada)
Fundersnot available
KeywordsMedicineTestosterone (patch)Chronic liver diseaseAlcoholic liver diseaseCirrhosisInternal medicineLiver diseaseSex hormone-binding globulinRetrospective cohort studyLibidoOutpatient clinicFatty liverGynecomastiaGastroenterologyLiver function testsDiseasePhysiologyHormoneAndrogen

Abstract

fetched live from OpenAlex

Objective: Loss of libido, low serum testosterone levels, and other symptoms of hypogonadism like subfertility, gynecomastia, and immature testes, is a prevalent medical condition amongst males with advanced chronic liver disease. The purpose of this investigation was to evaluate the low serum testosterone levels association with hypogonadism in people with chronic liver disease. Study Duration: This study was carried out at Outpatient Department (OPD) of Medicine Ayub Teaching Hospital, Abbottabad from 1st January 2022 to 30th June 2022. Material and Methods: The retrospective study was completed on two hundred confirmed patients of hypogonadism with liver cirrhosis. In the repository, the available data was divided into two groups. The first group of chronic liver disease patients was diagnosed due to non-alcoholic fatty liver disease (NAFLD) and second patient group due to alcoholic liver disease (ALD). The patient in NAFLD group were in the age group between 15-30 years whereas the patients from ALD group were 30-60 years of age. The diagnostic values of total testosterone and Sex hormone binding globulin (SHBG) were collected from patient’s record. The independent t test was used for statistical analysis by using SPSS version 22. The frequency distribution of testosterone was also calculated between two types of chronic liver disease patients. Results: The retrospective research was performed. The data were dispersed across two age groups. The youth had no alcohol-related data, while the elderly had. According to hospital data, distribution was based on age between 15 and 30 years (NAFLD) and between 30 and 60 years (ALD). When T-test was applied it showed that there was no statistically significant difference found in means of SHBG between two age groups amongst chronic liver disease patients. In the case of serum testosterone, there was a statistically significant (p 0.05) difference between age groups (Table 1). Figures 1 and 2 illustrate the frequency distribution of total testosterone and its comparison within the liver cirrhosis group, respectively. Practical Implication: Our study predicted that low testosterone can raise the risk of mortality, the necessity for liver transplantation, and the likelihood of severe infection in men with cirrhosis given the mechanisms of action of testosterone. Conclusion: On the basis of two groups of liver cirrhosis, there is a significant age-related change in total serum testosterone, its control by the pituitary and it’s binding to SHBG in males over the age of forty. However, no substantial data was discovered in sex hormone binding globulin. Low testosterone levels are associated with hypogonadism in patients with cirrhosis of the liver. The effect of testosterone replacement treatment on increasing muscle quality in male cirrhotic patients remains to be determined. Safety and effectiveness of the treatment requires additional prospective research. Keywords: Sex Hormone Binding Globulin, Serum Testosterone, Hypogonadism, Chronic Liver Disease

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.027
GPT teacher head0.219
Teacher spread0.192 · 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 source (direct Gemma or distilled Codex), 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
Published2022
Admission routes1
Has abstractyes

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