The Tangible Common Denominator of Substance Use Disorders: A Reply to Commentaries to Rehm et al. (2013a)
Bibliographic record
Abstract
In response to our suggestion to define substance use disorders via 'heavy use over time', theoretical and conceptual issues, measurement problems and implications for stigma and clinical practice were raised. With respect to theoretical and conceptual issues, no other criterion has been shown, which would improve the definition. Moreover, heavy use over time is shown to be highly correlated with number of criteria in current DSM-5. Measurement of heavy use over time is simple and while there will be some underestimation or misrepresentation of actual levels in clinical practice, this is not different from the status quo and measurement of current criteria. As regards to stigma, research has shown that a truly dimensional concept can help reduce stigma. In conclusion, 'heavy use over time' as a tangible common denominator should be seriously considered as definition for substance use disorder.
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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 | 0.015 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.078 | 0.085 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".