Suggestions for Canada’s Opioid Use Disorder Management Guidelines
Bibliographic record
Abstract
1Centre for Addiction and Mental Health, Toronto, ON, Canada 2Department of Psychiatry, University of Toronto, Toronto, ON, Canada Corresponding Author: Robert A. Kleinman, MD, 100 Stokes Street, 3rd Floor, Toronto, ON, Canada, M6J 1H4. Tel: 416-535-8501; fax: 416-595-6821. E-mail: [email protected] Dr. R.A.K. has received research funding through the Centre for Addiction and Mental Health Discovery Fund and research funding and training support through the Research in Addiction Medicine Scholars Program, R25DAO33211 (NIDA) and travel awards from the American Psychiatric Association and American Academy of Addiction Psychiatry. Dr R.A.K. has been invited to take part in the review of the upcoming CRISM National Guideline for Clinical Management of OUD when they become available in 2023 as part of the panel of reviewers from Ontario but has not had any further involvement in the review or other aspects of the project.
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 | 0.042 | 0.161 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.016 | 0.014 |
| Insufficient payload (model declined to judge) | 0.032 | 0.011 |
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".