Incidence and predictors of non-hepatic cancers in biopsy-proven alcohol-related liver disease
Bibliographic record
Abstract
INTRODUCTION AND OBJECTIVES: Alcohol-related liver disease (ALD) is a known contributor to non-hepatic cancers (NHC). We aimed to describe the incidence and predictors of NHC in patients with ALD. MATERIALS AND METHODS: The WALDO study is a multicenter cohort study of patients with histologically characterized ALD. Participants are followed from the time of liver biopsy and outcomes are captured from health records. The primary outcome was the incidence of the first NHC. Risk factors for NHC were presented as unadjusted and adjusted sub-distribution hazards (SDH) based on competing risk analysis. Statistical analyses were done in R. RESULTS: 694 patients were included. The median age was 51 years (IQR 43- 59), 428 (62 %) patients were male and 349 (50 %) had cirrhosis on biopsy. During a median follow-up of 4.9 years (IQR 1.3 - 9.6 years), 78 patients (11 %) with ALD developed NHC. The cumulative incidence of NHC in ALD was 2.4 % (1.4 - 3.9 %) over five years. The most common site of NHC was respiratory (17 cases, 22 % of NHC) and digestive tract (17 cases, 22 %) and cancers of the head and neck (13 cases, 17 %). On multivariable analysis, increasing age (SDH 1.03; CI 1.00-1.07; p=0.037), previous smoking (SDH 5.11; CI 1.91-13.66; p= 0.001) and current smoking (SDH 3.84; CI 1.50-9.84; p = 0.005) along with cirrhosis (SDH 2.14; CI 1.10 -4.13; p=0.024) were associated with a higher risk of NHC. CONCLUSIONS: NHC's are common in patients with biopsy-confirmed ArLD. Addressing risk factors for NHC's should be encouraged during routine follow-up to reduce the incidence.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".