MétaCan
Menu
Back to cohort
Record W3009292207 · doi:10.1097/adm.0000000000000643

An Acute Care Contingency Management Program for the Treatment of Stimulant Use Disorder: A Case Report

2020· article· en· W3009292207 on OpenAlexaffabout
Paxton Bach, Emma Garrod, Kaye Robinson, Nadia Fairbairn

Bibliographic record

VenueJournal of Addiction Medicine · 2020
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicForensic Toxicology and Drug Analysis
Canadian institutionsBritish Columbia Centre on Substance UseProvidence Health Care
FundersNational Institute on Drug Abuse
KeywordsContingency managementMedicineStimulantCase managementContingency planAcute careAcute medicinePsychiatryIntensive care medicineHealth careIntervention (counseling)

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.885
Threshold uncertainty score0.360

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.079
GPT teacher head0.448
Teacher spread0.369 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Addiction MedicineSame topicForensic Toxicology and Drug AnalysisFrench-language works237,207