The Alcohol Hangover Research Group Consensus Statement on Best Practice in Alcohol Hangover Research
Bibliographic record
Abstract
Alcohol-induced hangover, defined by a series of symptoms, is the most commonly reported consequence of excessive alcohol consumption. Alcohol hangovers contribute to workplace absenteeism, impaired job performance, reduced productivity, poor academic achievement, and may compromise potentially dangerous daily activities such as driving a car or operating heavy machinery. These socioeconomic consequences and health risks of alcohol hangover are much higher when compared to various common diseases and other health risk factors. Nevertheless, unlike alcohol intoxication the hangover has received very little scientific attention and studies have often yielded inconclusive results. Systematic research is important to increase our knowledge on alcohol hangover and its consequences. This consensus paper of the Alcohol Hangover Research Group discusses methodological issues that should be taken into account when performing future alcohol hangover research. Future research should aim to (1) further determine the pathology of alcohol hangover, (2) examine the role of genetics, (3) determine the economic costs of alcohol hangover, (4) examine sex and age differences, (5) develop common research tools and methodologies to study hangover effects, (6) focus on factor that aggravate hangover severity (e.g., congeners), and (7) develop effective hangover remedies.
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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 | 0.271 | 0.359 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.016 | 0.010 |
| Research integrity | 0.025 | 0.024 |
| Insufficient payload (model declined to judge) | 0.007 | 0.009 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".