Implementation of Clinical Algorithms for Take-Home Naloxone and Buprenorphine/ Naloxone in Emergency Rooms: SuboxED Project Evaluation
Bibliographic record
Abstract
Introduction: Emergency departments (EDs) are often the first point of care for people at risk of opioid-related overdose, an issue on the rise in Canada. Dispensing take-home naloxone (THN) and/or initiating opioid agonist treatment (OAT) in the ED can help prevent overdose. Methods: The SuboxED (CC-BY-NC-SA) project evaluated the implementation of a clinical algorithm for dispensing THN and prescribing buprenorphine/naloxone (B/n) in three EDs in the province of Québec. We performed a retrospective review of ED electronic medical records flagged as “at risk of opioid overdose (ROO).” This study included an implementation process from April 1, 2018 to April 30, 2019, and an evaluation of the project implementation for eligible patients from May 1 to December 31, 2019. We also administered satisfaction surveys to medical teams and patients. Results: A total of 877 (36.2%) patient records were included in the analysis. Of these, 62% had a confirmed diagnostic of opioid use disorder (OUD) and 70.8% met eligibility criteria for naloxone prescription. However, only 7.7 % were given a prescription or take-home naloxone in the ED, and 12.4 % were initiated on B/n in the ED or in the community after the ED visit. Seven patients and 125 health care providers from EDs, clinics, and retail pharmacies completed the survey. Conclusion: The SuboxED project demonstrated the feasibility of implementing a clinical algorithm for dispensing THN and initiating B/n in the ED, and of evaluating its efficacy in the 6 months following implantation. In addition to advocating for free access to THN in EDs, scaling up the uptake of the algorithm in EDs is the next challenge.
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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 | 0.064 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".