Neuromodulation and Opioid Use Disorder: Ethical Opportunities for Canada
Bibliographic record
Abstract
Despite increased efforts of government and non-government organisations to intervene via harm reduction and education initiatives, the opioid crisis has continued to worsen and has been exacerbated by the COVID-19 pandemic. In British Columbia, Canada, opioid overdose deaths in 2021 are the highest ever recorded. Neuromodulation procedures such as deep brain stimulation and repetitive transcranial magnetic stimulation have gained traction as treatments for opioid use disorder in various countries such as Germany, the Netherlands, the United States and China. However, these treatment options have been met with apprehension from both clinicians and patients, likely owing to fear, stigma and reluctance to label addiction as a brain disorder. Further complicating this landscape are socio-demographic factors, as marginalised communities are disproportionately burdened by addiction, while having poor access to care and a history of distrust in the health system. This multifactorial challenge involving many sociocultural factors requires culturally sensitive, interdisciplinary approaches to ensure direct-to-brain innovations are implemented ethically and equitably. This review summarises the state of the science for using neuromodulation to treat opioid use disorder, as well as the available ethical discourse surrounding the expansion of clinical trials and eventual widespread clinical implementation. Additional ethics discussions highlight opportunities for the engineering and clinical evolution of neuromodulation for opioid use disorder trials.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".