Alcohol Consumption and Liver Disease in Australia: A Time Series Analysis of the Period 1935–2006
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
AIMS: The aim of the study was to examine for Australia whether the link between population alcohol consumption and liver disease mortality varies over time, using 71 years of data. METHODS: Overall and gender-specific rates of liver disease mortality were analysed in relation to total alcohol consumption as well as for different beverage types by using autoregressive integrated moving average (ARIMA) time series methods. Separate models were developed for the entire time period and for two sub-periods (1935-1975, 1976-2006). RESULTS: A 1-l increase in adult per capita consumption of pure alcohol led to a rise of ∼10% in overall liver disease mortality rates and a 11 and 9% increase in female and male liver disease mortality, respectively. The strength of the relationship between per capita consumption and liver disease mortality diminished over time. Spirits consumption was found to be the main driving factor in liver mortality rates between 1935 and 1975, while beer consumption was found to be the most significant predictor in liver diseases in the last three decades. In a comparative perspective, the effect of per capita alcohol consumption on liver disease in Australia is similar to the USA, Southern and Eastern Europe countries, but weaker than in Canada and western European countries. CONCLUSION: An increase in per capita alcohol consumption in Australia is likely to lead to an increase in liver disease. Changes in the most important beverage over the study period suggest substantial shifts in drinking patterns and preferences among the heaviest Australian drinkers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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