Bibliographic record
Abstract
The relationship between alcohol consumption and all-cause mortality is J-shaped in most industrialized countries. The J-shape is the result of the combination of adverse and beneficial effects of alcohol consumption. Adverse effects include several types of cancer (oropharyngeal, oesophageal, liver, laryngeal and breast cancer), other diseases of the aerodigestive tract, diseases of the heart (alcoholic cardiomyopathy, haemorrhagic stroke, arrhythmia, hypertension), addiction-related mental disorders, and accidents and injuries. Beneficial effects are for ischaemic heart disease and ischaemic stroke. The exact shape of the all-cause mortality curve in a given region depends upon the proportion of the population consuming alcohol at different levels, especially heavy consumption, and on the prevalence of the disorders named above. Thus regions with a relatively low prevalence of ischaemic cardiovascular disease show almost no benefits of consumption, and an all-cause mortality curve which is almost exponential. Females experience a minimum mortality risk at a level of alcohol intake which is lower than that associated with the minimum risk for men. Similarly, an upturn in mortality risk occurs at lower intake levels for women than for men. At present, there is no satisfactory explanation for the observation that the shape of the mortality curve varies with the consumption level of the cohort under study. Heavier-drinking cohorts tend to display their minimum risk at relatively higher levels of alcohol intake than cohorts with lower alcohol consumption.
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".