A rapid access to addiction medicine clinic facilitates treatment of substance use disorder and reduces substance use
Bibliographic record
Abstract
BACKGROUND: Substance use is prevalent in Canada, yet treatment is inaccessible. The Rapid Access to Addiction Medicine (RAAM) clinic opened at the University Health Network (UHN) in January 2018 as part of a larger network of addictions clinics in Toronto, Ontario, to enable timely, low barrier access to medical treatment for substance use disorder (SUD). Patients attend on a walk-in basis without requiring an appointment or referral. We describe the RAAM clinic model, including referral patterns, patient demographics and substance use patterns. Secondary outcomes include retention in treatment and changes in both self-reported and objective substance use. METHODS: The Electronic Medical Record at the clinic was reviewed for the first 26 weeks of the clinic's operation. We identified SUD diagnoses, referral source, medications prescribed, retention in care and self-reported substance use. RESULTS: The clinic saw 64 unique patients: 66% had alcohol use disorder (AUD), 39% had opiate use disorder (OUD) and 20% had stimulant use disorder. Fifty-five percent of patients were referred from primary care providers, 30% from the emergency department and 11% from withdrawal management services. Forty-two percent remained on-going patients, 23% were discharged to other care and 34% were lost to follow-up. Gabapentin (39%), naltrexone (39%), and acamprosate (15%) were most frequently prescribed for AUD. Patients with AUD reported a significant decrease in alcohol consumption at their most recent visit. Most patients (65%) with OUD were prescribed buprenorphine, and most patients with OUD (65%) had a negative urine screen at their most recent visit. CONCLUSION: The RAAM model provides low-barrier, accessible outpatient care for patients with substance use disorder and facilitates the prescription of evidence-based pharmacotherapy for AUD and OUD. Patients referred by their primary care physician and the emergency department demonstrated a reduction in median alcohol consumption and high rates of opioid abstinence.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.044 | 0.003 |
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".