Opioid Prescribing In-Hospital: Time for Innovative Approaches to Help Combat the Opioid Crisis
Bibliographic record
Abstract
ABSTRACT Inappropriate opioid prescribing has been well recognized as a major contributor to North America's current opioid epidemic. Despite this, hospitals have largely been overlooked as a potential setting responsible for the development and management of opioid use disorder. This commentary examines acute care settings as a risk environment associated with opioid use disorder and discusses several innovative strategies to address existing challenges in hospital environments. Résumé La prescription d’opioïdes inappropriés a été largement reconnue comme étant l’une des principales causes de l’épidémie actuelle d’opioïdes en Amérique du Nord. Malgré cela, les hôpitaux ont été largement négligés en tant que cadre potentiel responsable du développement et de la gestion du trouble de l’usage des opioïdes (TLUO). Ce commentaire examine les établissements de soins de courte durée en tant qu’environnement de risque associé aux TLUO et examine plusieurs stratégies novatrices pour relever les défis existants en milieu hospitalier.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.012 | 0.018 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".