Why Not Add Consumption Measures to Current Definitions of Substance Use Disorders? Commentary on Rehm et al. ‘Defining Substance Use Disorders: Do We Really Need More Than Heavy Use?’
Bibliographic record
Abstract
The article by Rehm and colleagues in this issue of the Journal argues that diagnoses of substance use disorders should be based solely on measures of consumption. Although the authors provide convincing arguments for inclusion of consumption measures in the diagnostic criteria for substance use disorders, we do not agree that diagnostic criteria should be restricted to measures of consumption alone. Our clinical and research experience with alcohol use disorders suggests that use of consumption measures alone would fail to identify many patients whose alcohol or drug use is adversely impacting their health. Instead, we advocate-as others have done-that measures of consumption be added to current diagnostic criteria.
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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 | 0.016 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.058 | 0.066 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
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".