New perspectives on how to formulate alcohol drinking guidelines: Response to commentaries
Bibliographic record
Abstract
There is a need for research on how the use of years of life lost and risk zones affect risk perception, and how risk acceptability differs between individuals. Furthermore, there is a need for guidance documents to better delineate how single-occasion drinking increases harms. We would like to thank Mr Angus and Drs Livingston, Holmes and Greenfield for their commentaries on our debate article [1-4]. These commentaries focus upon (i) the definition, operationalization and communication of health consequences from alcohol use and (ii) the need to communicate how per occasion drinking impacts health. The Canadian Guidance on Alcohol and Health (CGAH) uses years of life lost (YLL) as the main measure of health loss. However, as noted by the commentators, while YLL are more comprehensive of health loss when compared to death, there is a need for evidence on how the use of YLL affects people's perceptions of alcohol's impact upon health [5]. As noted by the commentators, further research and consultation is needed on the acceptability of alcohol-related risks, and to determine what constitutes low-, moderate- and high-risk. For example, is it appropriate to use Starr's analysis, as we did, or should we determine different risk zone thresholds for different activities? How, then, do we help people compare risk-taking behaviours if variable criteria are used under different circumstances? We acknowledge that the effect of the CGAH risk zone approach (as opposed to a single threshold approach) on a person's perceptions of alcohol's impact upon health are unknown, as there is no evidence on this topic [6]. The CGAH risk zones correspond to Starr's analysis of people's willingness to accept risks from voluntary behaviours [7]. Rather than defining an acceptable risk level, we used the widely accepted 1:1000 and 1:100 life-time-attributable death thresholds as bench-marks to communicate risk. One benefit of using risk zones is that they promote autonomy by avoiding the endorsement of any particular risk threshold as acceptable. Their use also may increase the relevance of guidelines to those for whom consumption at the lowest risk zone may not be possible or welcome. Therefore, we hypothesize that the most effective way to communicate the impact of alcohol use on health is to provide risk information based on a standard risk definition, with each individual determining what risk is acceptable to him or her. In addition to guidance documents, personalized web applications may help to operationalize risk measures in a variety of forms, including death, loss of life expectancy, incidence of disease, financial costs and calorie intake, while also enabling the autonomy to select desired health gains through reduced drinking. Dr Greenfield called attention to the CGAH's limitations in conveying the risks of per occasion drinking [6]. The CGAH recommends that ‘you don’t exceed 2 drinks on any day’ (where a drink is 13.45 g of ethanol). While it is not possible to model the impacts of both volume and patterns of alcohol use due to the availability of suitable risk ratios, we agree with Dr Greenfield that there is sufficient evidence for guidance documents to discuss how a greater number of drinks consumed on an occasion increases harms, and that this message needs to be better emphasized in future knowledge mobilization of the CGAH. Catherine Paradis (co-chair), Peter Butt (co-chair), Mark Asbridge, Danielle Buell, Samantha Cukier, Francois Damphousse, Jennifer Heatley, Erin Hobin, Harold R. Johnson, Ryan McCarthy, Chris Mushquash, Daniel Myran, Tim Naimi, Nancy Poole, Justin Presseau, Adam Sherk, Kevin D. Shield, Tim Stockwell, Sharon Straus, Kara Thompson, Samantha Wells and Matthew Young. Kevin D. Shield: Conceptualization (equal); writing—original draft (equal); writing—review and editing (equal). Catherine Paradis: Conceptualization (equal); funding acquisition (equal); writing—original draft (equal); writing—review and editing (equal). Peter R. Butt: Conceptualization (equal); funding acquisition (equal); writing—original draft (equal); writing—review and editing (equal). Timothy Naimi: Conceptualization (equal); writing—review and editing (equal). Adam Sherk: Conceptualization (equal); writing—review and editing (equal). Mark Asbridge: Conceptualization (equal); writing—review and editing (equal). Daniel T. Myran: Conceptualization (equal); writing—original draft; writing—review and editing (equal). Tim Stockwell: Conceptualization (equal); writing—review and editing (equal). Samantha Wells: Conceptualization (equal); writing—review and editing (equal). Nancy Poole: Conceptualization (equal); writing—review and editing (equal). Jennifer Heatley: Conceptualization (equal); writing—review and editing (equal). Erin Hobin: Conceptualization (equal); writing—review and editing (equal). Kara Thompson: Conceptualization (equal); writing—review and editing (equal). Matthew M. Young: Conceptualization (equal); writing—review and editing (equal). Production of this document was made possible by a financial contribution from Health Canada to the Canadian Centre on Substance Use and Addiction. The views expressed herein do not necessarily represent the views of Health Canada. T.S. and A.S. declare funding from the Ontario Public Service Employees Union to serve as expert witnesses opposing the introduction of alcohol sales in corner stores. T.N., T.S. and A.S. declare funding from the Finnish government alcohol monopoly, Alko, to conduct research on alcohol and public health policy. T.N. declares funding from the British Columbia Liquor and Cannabis Regulation Branch.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".