Take home injectable opioids for opioid use disorder during and after the COVID-19 Pandemic is in urgent need: a case study
Bibliographic record
Abstract
BACKGROUND: In North America the opioid poisoning crisis currently faces the unprecedented challenges brought by the COVID-19 pandemic, further straining people and communities already facing structural and individual vulnerabilities. People with opioid use disorder (OUD) are facing unique challenges in response to COVID-19, such as not being able to adopt best practices (e.g., physical distancing) if they're financially insecure or living in shelters (or homeless). They also have other medical conditions that make them more likely to be immunocompromised and at risk of developing COVID-19. In response to the COVID-19 public health emergency, national and provincial regulatory bodies introduced guidance and exemptions to mitigate the spread of the virus. Among them, clinical guidance for prescribers were issued to allow take home opioid medications for opioid agonist treatment (OAT). Take Home for injectable opioid agonist treatment (iOAT) is only considered within a restrictive regulatory structure, specific to the pandemic. Nevertheless, this risk mitigation guidance allowed carries, mostly daily dispensed, to a population that would not have access to it prior to the pandemic. In this case it is presented and discussed that if a carry was possible during the pandemic, then the carry could continue post COVID-19 to address a gap in our approach to individualize care for people with OUD receiving iOAT. CASE PRESENTATION: Here we present the first case of a patient in Canada with long-term OUD that received take home injectable diacetylmorphine to self-isolate in an approved site after being diagnosed with COVID-19 during a visit to the emergency room where he was diagnosed with cellulitis and admitted to receive antibiotics. CONCLUSION: In the present case we demonstrated that it is feasible to provide iOAT outside the community clinic with no apparent negative consequences. Improving upon and making permanent these recently introduced risk mitigating guidance during COVID-19, have the potential not just to protect during the pandemic, but also to address long-overdue barriers to access evidence-based care in addiction treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".