Opioid Use Disorder: Screening, Diagnosis, and Management
Bibliographic record
Abstract
Over the past decade, the opioid crisis in Canada has been worsening. In 2019, over 3,800 people across Canada died due to an apparent opioid-related cause, which represents a 26% increase from just 3 years prior. Given North America’s ongoing opioid crisis, and the contribution opioid-prescribing practices have had to date, a critical need exists to ensure that health care providers are not only educated about safe opioid prescribing but also are knowledgeable about how to effectively screen for, diagnose, and treat an individual with opioid use disorder. RésuméAu cours des dix dernières années, la crise des opioïdes au Canada n’a cessé de s’aggraver. En 2019, plus de 3 800 personnes au Canada sont décédées d’une cause apparemment liée à la consommation d’opioïdes, ce qui représente une augmentation de 26 % par rapport à seulement trois ans auparavant. Étant donné la crise des opioïdes qui sévit actuellement en Amérique du Nord et la contribution des pratiques de prescription d’opioïdes qui ont eu cours jusqu’ici, un besoin critique est à combler pour veiller à ce que les fournisseurs de soins soient non seulement formés sur la prescription sécuritaire des opioïdes, mais aussi bien informés sur le dépistage, le diagnostic et le traitement efficace d’un trouble lié à la consommation d’opioïdes.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".