The effectiveness of telemedicine-delivered opioid agonist therapy in a supervised clinical setting
Bibliographic record
Abstract
OBJECTIVE: Opioid use disorder has been declared a public health crisis across North America and opioid agonist therapy (OAT) is the standard of care for these patients. Despite the increasing adoption of telemedicine as a delivery method for OAT, its effectiveness has not yet been evaluated against traditional in-person treatment. This study compared treatment outcomes for in-person versus telemedicine-delivered OAT. METHODS: We conducted a non-randomized cohort comparison study using an administrative database for patients who commenced OAT between 2011 and 2012 across 58 clinic sites in the province of Ontario, Canada. Patients were stratified by primary treatment modality as being: in-person (<25% appointments by telemedicine), mixed (25-75% by telemedicine), or via telemedicine (>75% appointments by telemedicine). The primary outcome was continuous retention in treatment as defined by one year of uninterrupted therapy, based on pharmacy dosing records. RESULTS: A total of 3733 OAT initiating patients were identified. Patients treated via telemedicine were more likely to be retained in therapy than patients treated in-person (n=1590; aOR=1.27; 95% CI 1.14-1.41; p<0.001). Telemedicine patients demonstrated a retention rate of 50% at one year whereas in-person patients were retained at a rate of 39%. The mixed group also had higher likelihood of retention than the in-person group (n=418; aOR=1.26; 95% CI 1.08-1.47; p=0.001) and had a retention rate of 47% at one year. CONCLUSION: Telemedicine may be an effective alternative to delivering in person OAT, and it has the potential to expand access to care in rural, remote, and urban regions.
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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 | 0.003 | 0.015 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".