Binge drinking does not appear to have an adverse effect on non-alcoholic fatty liver disease: Findings from a study of four First Nations communities
Bibliographic record
Abstract
BACKGROUND: Binge drinking and non-alcoholic fatty liver disease (NAFLD) are common health problems throughout the world. However, the impact of binge drinking on NAFLD has yet to be described. The objective of this study was to document the extent of liver disease in community-based NAFLD patients who self-reported monthly binge drinking and compare the findings to NAFLD patients from the same communities who denied binge drinking (controls). METHODS: The study was undertaken in four Manitoba First Nations communities where the sale and consumption of alcoholic beverages are prohibited but visits to urban centres are common. Binge drinkers were retrospectively matched 1:2 by age, sex, and body mass index (BMI) with controls. NAFLD was diagnosed by ultrasonographic features of excess fat in the liver in individuals with no alternative, non-metabolic explanation for fatty infiltration of the liver. Hepatic inflammation and function were determined by standard liver biochemistry testing and fibrosis by FIB-4 levels and hepatic elastography. RESULTS: Of 546 NAFLD patients, 88 (16%) attested to binge drinking. The mean age of binge drinkers was 40 (SD 13) years; 51% were male; and the mean BMI was 34 (SD 7). Compared with controls, binge drinkers had similar liver biochemistry results (alanine and aspartate aminotransferases: 41 [SD 39] and 36 [SD 30] versus 36 [SD 36] and 31 [SD 27] U/L, p = 0.35 and p = 0.37, respectively), FIB-4 values (0.75 [SD 0.55] versus 0.72 [SD 0.44], p = 0.41, respectively), and hepatic elastrography (6.6 [SD 3.9] versus 6.2 [SD 2.9] kPa, p = 0.37, respectively) findings. CONCLUSIONS: In this study population, monthly binge drinking did not appear to impact the severity of NAFLD.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".