Frequency of type 2 diabetes mellitus in patients with chronic hepatitis C virus infection presenting to a tertiary care hospital
Bibliographic record
Abstract
Background: Chronic Hepatitis C Virus (HCV) infection poses significant public health challenges, not only due to its hepatic-related complications but also its association with metabolic disorders including Type 2 Diabetes Mellitus (T2DM). Understanding the frequency and contributing factors of T2DM in HCV patients is crucial for improving diagnosis, treatment, and outcomes Methods: This prospective study was conducted at the Medical Unit of Lady Reading Hospital, Peshawar Pakistan, from April to September 2021. A total of 200 HCV-seropositive patients aged 18–75 years were included. The diagnosis of HCV was confirmed through ELISA, and T2DM was determined based on HbA1c levels. Data on demographics, clinical parameters, and laboratory findings were collected. Statistical analysis was done which included univariate and multivariate logistic regression, to identify significant predictors of T2DM. Results: The prevalence of T2DM among HCV-seropositive patients was 37%. Key risk factors for T2DM included cirrhosis (OR = 2.005, 95% CI: 1.15–3.43), age ≥40 years, obesity, male gender, and a family history of diabetes (P < 0.05). Patients with cirrhosis had a significantly higher prevalence of T2DM, especially those aged >60 years. Overall, 61% of participants were overweight, 19% were obese, and 20% had normal BMI. Multivariate analysis highlighted cirrhosis and metabolic factors as significant contributors to T2DM risk. Conclusion: T2DM is highly prevalent among chronic HCV patients, particularly those with older age, having liver cirrhosis, obesity, and a family history of diabetes. Early screening and integrated management of metabolic and liver-related complications are essential for improving patient outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".