A synthesis of oral morphine equivalents (OME) for opioid utilisation studies
Bibliographic record
Abstract
PURPOSE: Oral Morphine Equivalent (OME) doses are increasingly being used as a metric to represent opioid use. Driven by a growing need from pharmacoepidemiological studies, the objective of this study was to develop a comprehensive OME conversion table that can be used by researchers to calculate OMEs in a consistent and systematic way. METHODS: Clinical guidelines and literature sources were collated and synthesised to develop recommended OME conversion factors that can be used for research studies on opioids (including different formulations and routes of administration) currently available internationally including Australia, the United Kingdom, Europe, the United States and Canada. RESULTS: No single resource includes all opioids that are currently available. Although there was some variation in conversion factors reported in different sources, overall, suggested conversion factors were mostly consistent across national and international sources. CONCLUSIONS: The use of the OME metric appears optimal for opioid utilisation studies as it facilities both interpretation and comparison between opioids and geographical locations. We have presented a synthesis of published OME conversion factors that can be applied to pharmacoepidemiological studies of opioids, in addition to a discussion of the considerations and caveats in using OME as a metric for opioid use. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.061 | 0.211 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.055 | 0.045 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".