High Body Mass Index Is An Independent Risk Factor for Nonresponse to Antiviral Treatment in Chronic Hepatitis C
Bibliographic record
Abstract
The aim of this study was to determine if body mass index (BMI) was an independent predictor of response to antiviral treatment in patients with chronic hepatitis C. A retrospective review was performed of all patients at a single center with chronic hepatitis C treated with antiviral medication from 1989 to 2000. A sustained response was defined as either negative hepatitis C virus (HCV) RNA by polymerase chain reaction and/or normal alanine aminotransferase (ALT) level (only in those treated before availability of HCV RNA testing) 6 months following completion of therapy. All patients were classified into one of 3 groups according to BMI (normal, <25 kg/m(2); overweight, 25-30 kg/m(2); obese, >30 kg/m(2)). A total of 253 patients were treated with either interferon (IFN) monotherapy or IFN in combination with ribavirin. Patients were excluded if predetermined clinical characteristics were unavailable. Using logistic regression, and after adjusting for the examined variables (age, sex, history of alcohol consumption >50 g/d, cirrhosis on pretreatment biopsy, and BMI), likelihood ratio tests showed significant differences in response to treatment according to BMI group (P =.01), genotype (P <.01), and cirrhosis (P <.01). Those with genotypes 2 or 3 had an odds ratio (OR) for success of 11.7 compared with those with genotype 1, cirrhotic patients had an OR of 0.15 compared with noncirrhotic patients, and obese patients had an OR of 0.23 compared with normal and overweight patients. Hepatic steatosis was not an independent risk factor for response to antiviral treatment. In conclusion, obesity, only when defined as a BMI greater than 30 kg/m(2), is an independent (of genotype and cirrhosis) negative predictor of response to hepatitis C treatment.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".