A203 INCIDENCE AND PREDICTORS OF NON-HEPATIC CANCERS IN ALCOHOL RELATED LIVER DISEASE IN THE WALDO COHORT
Bibliographic record
Abstract
Abstract Background Alcohol-related liver disease (ArLD) accounts for 48% of cirrhosis-related deaths in the world. Alcohol is a known contributor to hepatocellular carcinoma (HCC) and non-hepatic cancers (NHC). Aims We aimed to describe the incidence and predictors of NHC in patients with histologically characterized ArLD. Methods Data came from an international, multicenter retrospective cohort study of patients with histologically characterized ArLD. We excluded all patients with clinical, biochemical, or histologic evidence of liver disease due to another etiology noted either at diagnosis or during follow-up. The primary outcome was incidence of the first of NHC. To identify risk factors of NHC, baseline characteristics of patients with and without NHC were compared. Outcomes were presented as unadjusted and adjusted hazard ratios (HR) based on COX analysis. All statistical analysis was done in R using the ‘cmprsk’ package. Results A total of 633 patients with histologically characterized ArLD were included. The mean age was 51 years, 64% of patients were male and 58% had cirrhosis on biopsy. We found that 69 patients with ArLD (11%) developed NHC during a median follow-up of 8.8 years. The most common NHC was lung cancer (19%). Other cancers are shown in Figure 2. Patients who developed NHC were older (55 vs. 50 years old; p<0.0001) compared to those without NHC. BMI, current or past smoking status, peak alcohol use, absence of cirrhosis and histological findings did not differ between groups. On multivariable analysis, past smoking status (HR 5.70, p=0.002) and current smoking status (HR 4.95; p = 0.003) were associated with higher rates of NHC. Conclusions In this large multicenter cohort study, we found ArLD is associated with an increased incidence of NHCs, primarily lung cancer. Past and current smoking are risk factors associated with an increased risk of NHC in ArLD. Funding Agencies None
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".