Building an inpatient addiction medicine consult service in Sudbury, Canada: preliminary data and lessons learned in the era of COVID-19
Bibliographic record
Abstract
OBJECTIVE: The goal of this study was to (1) Describe the patient population of a newly implemented addiction medicine consult service (AMCS); (2) Evaluate referrals to community-based addiction support services and acute health service use, over time; (3) Provide lessons learned. METHODS: A retrospective observational analysis was conducted at Health Sciences North in Sudbury, Ontario, Canada, with a newly implemented AMCS from November 2018 and July 2021. Data were collected using the hospital's electronic medical records. The outcomes measured included the number of emergency department visits, inpatient admissions, and re-visits over time. An interrupted time-series analysis was performed to measure the effect of AMCS implementation on acute health service use at Health Sciences North. RESULTS: A total of 833 unique patients were assessed through the AMCS. A total of 1,294 referrals were made to community-based addiction support services, with the highest proportion of referrals between August and October 2020. The post-intervention trend for ED visits, ED re-visits, ED length of stay, inpatient visits, re-visits, and inpatient length of stay did not significantly differ from the pre-intervention period. CONCLUSION: Implementation of an AMCS provides a focused service for patients using with substance use disorders. The service resulted in a high referral rate to community-based addiction support services and limited changes in health service usage.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".