Effect of fibrosis on adverse events in patients with hepatitis C treated with telaprevir
Bibliographic record
Abstract
BACKGROUND: Data about adverse events are needed to optimise telaprevir-based therapy in a broad spectrum of patients. AIM: To investigate adverse events of telaprevir-based therapy in patients with and without advanced fibrosis or cirrhosis in a real-world setting. METHODS: Data on 174 hepatitis C-infected patients initiating telaprevir-based therapy at Mount Sinai and Montefiore medical centres were collected. Biopsy data and FIB-4 scores identified patients with advanced fibrosis. Multivariable fully adjusted models were built to assess the effect of advanced fibrosis on specific adverse events and discontinuation of treatment due to an adverse event. RESULTS: Patients with (n = 71) and without (n = 103) advanced fibrosis were similar in BMI, ribavirin exposure, gender, prior treatment history, haemoglobin and creatinine, but differed in race. Overall, 47% of patients completed treatment and 40% of patients achieved SVR. Treated patients with and without advanced fibrosis or cirrhosis had similar rates of adverse events; advanced fibrosis, however, was independently associated with ano-rectal discomfort (P = 0.03). Three patients decompensated and had advanced fibrosis. The discontinuation of all treatment medications due to an adverse event was significantly associated with older age (P = 0.01), female gender (P = 0.01) and lower platelets (P = 0.03). CONCLUSIONS: Adverse events were common, but were not significantly related to the presence of advanced fibrosis or cirrhosis. More critical monitoring in older and female patients with low platelets throughout treatment may reduce adverse event-related discontinuations.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".