Substance use disorder bridge clinics: models, evidence, and future directions
Bibliographic record
Abstract
BACKGROUND: The opioid overdose and polysubstance use crises have led to the development of low-barrier, transitional substance use disorder (SUD) treatment models, including bridge clinics. Bridge clinics offer immediate access to medications for opioid use disorder (MOUD) and other SUD treatment and are increasingly numerous. However, given relatively recent implementation, the clinical impact of bridge clinics is not well described. METHODS: In this narrative review, we describe existing bridge clinic models, services provided, and unique characteristics, highlighting how bridge clinics fill critical gaps in the SUD care continuum. We discuss available evidence for bridge clinic effectiveness in care delivery, including retention in SUD care. We also highlight gaps in available data. RESULTS: The first era of bridge clinic implementation has yielded diverse models united in the mission to lower barriers to SUD treatment entry, and preliminary data indicate success in patient-centered program design, MOUD initiation, MOUD retention, and SUD care innovation. However, data on effectiveness in linking to long-term care are limited. CONCLUSIONS: Bridge clinics represent a critical innovation, offering on-demand access to MOUD and other services. Evaluating the effectiveness of bridge clinics in linking patients to long-term care settings remains an important research priority; however, available data show promising rates of treatment initiation and retention, potentially the most important metric amidst an increasingly dangerous drug supply.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".