Quantitative and qualitative outcomes associated with inpatient addiction consultation: a scoping review
Bibliographic record
Abstract
Background: Rates of acute care use, including hospital admission and readmission, are high for people who misuse substances. Hospitalization provides a valuable opportunity for intervention, but addiction treatment is often not addressed in the inpatient setting. Addiction consult services are a novel intervention intended to change hospital practices.Objectives: Comprehensively summarize outcomes (quantitative and qualitative) associated with inpatient addiction consult services.Methods: English-language searches of: Medline, CINAHL, Embase, The Cochrane Database of Systematic Reviews, PubMed, PsychInfo and Google Scholar were conducted from 2000 to November 2022. Studies reporting outcomes associated with addiction specialist consultation in the hospital setting were included. Four independent reviewers screened abstracts, and three reviewers screened full-text articles.Results: A total of 1,113 results underwent title and abstract screening and 43 studies were included. Outcomes associated with addiction specialist consultation were heterogeneous. Quantitative clinical outcomes focused on pharmacotherapy, healthcare utilization, and outpatient follow-up. Consultation improved rates of pharmacotherapy use, but had inconsistent effects on health care use, and overall follow-up rates were low. Consultation was associated with reduced overdose rates and 90-day mortality. Additional outcomes related to medical learners’ educational achievements and qualitative results described positive effects on trainees, healthcare providers, and patients seen by specialized consult services. Access to dedicated providers improved experiences in hospitals for both people who misuse substances and their healthcare providers.Conclusion: Addiction specialist consultations are related to several clinical metrics, but some outcomes (e.g. pharmacotherapy initiation) may be more amenable to intervention than others (healthcare utilization). Qualitative findings provide important context for quantitative clinical results.
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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 | 0.061 | 0.248 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.039 | 0.041 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".