Health impact of alcohol use in the USA: a protocol of a systematic review and modelling study
Bibliographic record
Abstract
INTRODUCTION: Alcohol is consumed by an estimated 137.4 million people in the USA 12 years of age and older and, as a result, is estimated to have caused about 140 thousand deaths among people 20 to 64 years of age each year from 2015 up to and including 2019. METHODS: . A multi-method approach will be utilised to formulate conclusions on (i) weekly (ie, average) thresholds to minimise long-term and short-term risks of morbidity and mortality, (ii) daily thresholds to minimise the short-term risk of injury or acute illness due to per occasion drinking, (iii) alcohol use among vulnerable populations (eg, pregnant women) and (iv) situations and circumstances that are hazardous for alcohol use. To inform expert decisions, this project will also include a systematic review of existing low-risk drinking guidelines, a systematic review of meta-analyses which examine alcohol's impact on key attributable disease and mortality outcomes, and of estimates of the lifetime absolute risk of alcohol-attributable mortality and morbidity based on a person's sex and average level of alcohol use. The systematic reviews were designed in accordance with the preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P). The preliminary conclusions produced as a result of this project will undergo public consultation, and data from these consultations will be qualitatively analysed. The results of the public consultations will be used to further revise and refine the project's conclusions. ETHICS AND REGISTRATION: The study was granted an ethics exemption as only secondary data sources and unidentifiable public consultation will be utilised. Systematic reviews are pre-registered with PROSPERO (registration numbers CRD42024584924 and CRD42024584948). DISSEMINATION: , and for better informing individuals about the health risks associated with alcohol use.
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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 | 0.134 | 0.166 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.010 | 0.017 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.081 | 0.011 |
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".