Implementation and evaluation of a novel, unofficial, trainee-organized hospital addiction medicine consultation service
Bibliographic record
Abstract
Background To evaluate a novel, unofficial, trainee-organized, hospital addiction medicine consultation service (AMCS), we aimed to assess whether it was (1) acceptable to hospital providers and patients, (2) feasible to organize and deliver, and (3) impacted patient care. Methods We performed a retrospective descriptive study of all AMCS consultations over the first 16 months. We determined acceptability via the number of referrals received from admitting services, and the proportion of referred patients who consented to consultation. We evaluated feasibility via continuation/growth of the service over time, and the proportion of referrals successfully completed before hospital discharge. As most referrals related to opioid use disorder, we determined impact through the proportion of eligible patients offered and initiated on opioid agonist therapy (OAT) in hospital, and the proportion of patients who filled their outpatient prescription or attended their first visit with their outpatient OAT prescriber. Results The unofficial AMCS grew to involve six hospital-based residents and five supervising community-based addiction physicians. The service received 59 referrals, primarily related to injection opioid use, for 50 unique patients from 12 different admitting services. 90% of patients were seen before discharge, and 98% agreed to addiction medicine consultation. Among 34 patients with active moderate-severe opioid use disorder who were not already on OAT, 82% initiated OAT in hospital and 89% of these patients continued after discharge. Conclusions Established in response to identified gaps in patient care and learning opportunities, a novel, unofficial, trainee-organized AMCS was acceptable, feasible, and positively impacted patient care over the first 16 months. This trainee-organized, unofficial AMCS could be used as a model for other hospitals that do not yet have an official AMCS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".