An Acute Care Contingency Management Program for the Treatment of Stimulant Use Disorder: A Case Report
Bibliographic record
Abstract
BACKGROUND: Illicit stimulants such as crystal methamphetamine and cocaine are a rising cause of morbidity and mortality in North America. Unfortunately, there are few evidence-based approaches for the management of stimulant use disorder. Contingency management programs are currently the best evidenced treatment strategy, designed to reward behavior change and offer competing reinforcers toward the goal of reducing substance use, but these programs are often difficult to access. Given that it is well understood that hospitalization presents a valuable opportunity for the initiation of treatment for a variety of substance use disorders, the adaptation of contingency management programs to an acute medicine inpatient setting is a potentially viable option to improve care, and to increase access to effective treatment for stimulant use disorders. CASE SUMMARY: We present a case outlining the clinical care of a complex medical patient admitted with osteomyelitis, whose course in hospital changed significantly upon enrollment in a pilot contingency management program in an urban hospital in Canada. DISCUSSION: This case illustrates how effective treatment programs can be adapted as needed for use in novel settings, especially where current options are inaccessible, inadequate, or ineffective.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".