Assessment of alcoholic standard drinks using the Munich composite international diagnostic interview (M‐CIDI): An evaluation and subsequent revision
Bibliographic record
Abstract
The quantity and frequency of alcohol consumption are crucial both in risk assessment as well as epidemiological and clinical research. Using the Munich Composite International Diagnostic Interview (M-CIDI), drinking amounts have been assessed in numerous large-scale studies. However, the accuracy of this assessment has rarely been evaluated. This study evaluates the relevance of drink categories and pouring sizes, and the factors used to convert actual drinks into standard drinks. We compare the M-CIDI to alternative drink assessment instruments and empirically validate drink categories using a general population sample (n = 3165 from Germany), primary care samples (n = 322 from Italy, n = 1189 from Germany), and a non-representative set of k = 22503 alcoholic beverages sold in Germany in 2010-2016. The M-CIDI supplement sheet displays more categories than other instruments (AUDIT, TLFB, WHO-CIDI). Beer, wine, and spirits represent the most prevalent categories in the samples. The suggested standard drink conversion factors were inconsistent for different pouring sizes of the same drink and, to a smaller extent, across drink categories. For the use in Germany and Italy, we propose the limiting of drink categories and pouring sizes, and a revision of the proposed standard drinks. We further suggest corresponding examinations and revisions in other cultures.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".