Hepatotoxicity during legacy cancer chemotherapy in patients infected with hepatitis C virus: A retrospective cohort study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: The rates and causes of significant hepatotoxicity with cancer chemotherapy (CCT) in patients infected with hepatitis C virus (HCV) are incompletely characterized. METHODS: We compared rates of grade 3 or 4 hepatotoxicity, defined as elevated transaminases, during CCT in patients who are mono-infected with HCV compared with rates in controls matched on demographics, diagnosis, and rituximab use. We excluded patients with hepatobiliary cancers, hepatitis B virus or human immunodeficiency virus infection. Hepatotoxicity was attributed to a medical cause, cancer progression, or CCT, including HCV flare. RESULTS: Patients with HCV ( n = 196) had a higher rate of cirrhosis than the 1,130 matched controls (21.9% versus 4%; P <0.001). Their higher rate of overall hepatotoxicity (8.7% versus 4.5% of controls, P = 0.01) was due to higher rate of CCT-related hepatotoxicity (4.1% versus 1.2%, P = 0.01). On multivariable analysis, the largest risk factor for overall hepatotoxicity was cirrhosis, and the only risk factor for CCT-related hepatotoxicity was HCV infection. Among those with HCV, the only significant risk factor for hepatotoxicity was rituximab use. Hepatotoxicity caused by CCT delayed or altered treatment in only 3 HCV patients and 1 control (1.5% versus 0.1%, P = 0.01). CONCLUSIONS: Most patients with HCV can safely be treated with cancer chemotherapy. Cirrhosis and HCV infection contributed to increased hepatotoxicity in subjects on CCT. Among HCV patients, rituximab use was the major risk factor for increased hepatotoxicity. Hepatotoxicity due to CCT itself rarely altered or delayed CCT. Nonetheless, HCV-positive patients should be monitored carefully during CCT.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it