Review and Critique of Opioid Rotation Practices and Associated Risks of Toxicity
Bibliographic record
Abstract
OBJECTIVES: A dramatic increase in unintentional deaths from opioids has occurred over the past decade with strong inference that many of these deaths may be resulting from prescriber's error. Recent evidence suggests that the use of dose conversion ratios published in equianalgesic tables may lead to fatal or near-fatal opioid overdoses. The objective of this review was to determine whether the current practice of opioid rotation may be contributing to high rate of unintentional deaths. METHODS: We performed a focused literature review to identify reports of fatal or near-fatal outcomes that have occurred in conjunction with opioid rotation, to evaluate clinician competence in opioid rotation, and to identify inconsistencies in published protocols for opioid rotation. Further information was obtained by reviewing dosing instructions contained in product labels for extended-release formulations of several opioids. RESULTS: An increasing body of literature suggests that widely used opioid rotation practices, including the use of dose conversion ratios found in equianalgesic tables, may be an important contributor to the increasing incidence of opioid-related fatalities. These errors may be due, in part, not only to inadequate prescriber's competence but also to proliferation of inconsistent guidelines for opioid rotation, conflation of equianalgesic tables as conversion tables, and limitations inherent in the equianalgesic dose tables. CONCLUSIONS: Most of the fatal outcomes occurring during opioid rotation are preventable. The current process being used for opioid rotation has important flaws that must be corrected.
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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 | 0.020 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".