Twinning Research and Practice Guidelines in the Management of Addictions
Bibliographic record
Abstract
Dr Monica Zilberman and colleagues’ article on sex differences and psychiatric comorbidities in substance use disorders is the result of an international collaboration between the gender study group in Sao Paulo, Brazil; Dr. Sheila Blume, a pioneer in the field from the State University of New York; and our own group at the University of Calgary. This paper reports higher prevalence rates of psychiatric comorbidities with substance use disorders among women in both general population and treatment samples. A higher proportion of these comorbid disorders is primary among women, while the reverse is true for men. The authors outline the clinical characteristics of women with psychiatric comorbidity, particularly depression, and review the relevance of comorbidity to outcome. Despite its significance, analysis of sex difference is preliminary at best in pharmacologic and psychotherapeutic clinical trials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.052 | 0.166 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.018 | 0.024 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".