Alcohol consumption: the good, the bad, and the indifferent
Bibliographic record
Abstract
Dietary ethanol (alcohol) is the most widely consumed drug worldwide. High levels of mortality, morbidity, and social malaise are associated with abuse of alcohol, and increasing numbers of women and youth are abusing alcohol. However, strong epidemiological data demonstrate a U- or J-shaped relationship between volume of alcohol consumed and all-cause mortality or disease burden. Moderate alcohol consumption is associated with a lower risk of all-cause mortality and disease burden than are abstinence and immoderate drinking. A brief review of the absorption, distribution, metabolism, and excretion of ethanol is provided with a discussion of the impact of gender differences. Potential mechanisms by which ethanol, ethanol metabolites, and (or) phytochemicals, as associated with different types of ethanol-containing beverages, are discussed in regards to the beneficial and detrimental impacts they may have on physiological system functioning and mortality or disease burden. Per capita consumption of ethanol-containing beverages varies across geo-political regions worldwide. A more recent research focus is the impact of consumption patterns on consumption volumes as they relate to disease and mortality. Certain drinking patterns moderate overall volume of ethanol consumption. Thus, an emerging approach to the study of alcohol consumption in populations is to consider both the volume and pattern of consumption as they relate to mortality and disease burden. Alcohol consumption patterns among athletes are discussed; physiological implications of alcohol abuse in this population are outlined. Current guidelines for the consumption of alcohol are reviewed. Alcohol consumption guidelines reflect the current scientific understanding of both the benefits of moderate alcohol consumption and the detriments of immoderate 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".