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Record W4409174185 · doi:10.1111/add.70061

Commentary on Young <i>et al</i> .: Clinical guidance is required for stimulant co‐prescription with opioid agonist therapy

2025· article· en· W4409174185 on OpenAlexaboutno aff
Nadine Ezard, Krista J. Siefried, Brendan Clifford

Bibliographic record

VenueAddiction · 2025
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsnot available
FundersUniversity of New South Wales
KeywordsStimulantMedical prescriptionAgonistOpioidMedicinePsychologyOpioid-Related DisordersPsychiatryPsychotherapistAnesthesiaPharmacologyOpioid epidemicInternal medicineReceptor

Abstract

fetched live from OpenAlex

Young et al.'s [1] large Canadian cohort study showed no association between prescribed stimulants and opioid overdose among people on opioid agonist therapy, despite low rates of co-prescription, highlighting a need for clinical guidance on the management of coexisting opioid use disorder and attention-deficit hyperactivity disorder and/or stimulant use disorder. Young et al. [1] present new findings on the lack of a relationship between prescribed stimulants and opioid overdose from a Canadian cohort of people on opioid agonist therapy (OAT). The authors drew from a linked database of fatal and non-fatal opioid overdose in the province of British Columbia from 2015 to 2020. The authors found no association between stimulant co-prescription and overdose. Importantly, as the authors point out, the incidence of fatal overdose was low (1/500/year) [1], testimony to the protective role of OAT [2] and underscoring the importance of community access to effective OAT. Promoting effective initiation onto [3] and retention in [2] OAT is increasingly important in face of the rise of potent synthetic opioids and co-use with synthetic stimulants [4]. Stimulant prescription among people on OAT is an emerging therapeutic area that may have additional benefits over OAT alone. Prescription stimulants are first line pharmacotherapy for adults with attention-deficit hyperactivity disorder (ADHD) [5], which coexists with an important proportion of people with opioid use disorder (recent meta-analytic data suggest 20% [6]) and is associated with more severe opioid dependence and psychiatric comorbidity [7]. Young et al. [1] reported 4.4% of the 9395 participants had an ADHD diagnosis recorded; of those, 31% (212 participants) were prescribed a stimulant. Consistent with the literature [8], these data suggest a likely under-recognition and under-treatment of ADHD in an OAT population. Treatment of ADHD may improve retention in OAT [9]. Although the study was not designed to assess reasons for or patterns of stimulant prescription, more work is needed to explore the role of effective treatment of ADHD among people with opioid use disorder. Diagnosis of ADHD in adults with coexisting opioid use disorder (with or without concomitant non-prescribed stimulant use) is complicated by overlap in symptoms and lack of validated screening measures for substance use disorder populations. Consensus guidelines recommend routine screening and prompt diagnosis and treatment in people presenting with substance use disorder [10]. Emerging evidence suggests there may be a role for stimulant prescription for the management of stimulant use disorder at doses higher than for ADHD [11]. Co-use of stimulants with opioids is implicated in rising fatalities in North America, suggesting coexisting stimulant use disorder with opioid use disorder may be increasing at least in those countries. Yet only 38 people (0.4%) of the sample of 9395 people Young et al. [1] report on were noted to have a stimulant use disorder diagnosis, and only six (0.9%) of those prescribed a stimulant had a stimulant use disorder diagnosis. Indeed, the presence of a stimulant use disorder may preclude access to OAT [12]. Recent consensus guidelines for stimulant use disorder released by the American Society of Addiction Medicine and the American Academy of Addiction Psychiatry recommend consideration of off-label prescription of stimulants for the treatment of stimulant use disorder [13]. However, in the face of limited evidence for the treatment of coexisting opioid and stimulant use disorder, there are as yet no consensus OAT guidelines that comprehensively address concurrent treatment of stimulant use disorder. The Young et al. [1] data are drawn from administrative datasets and as such it is unclear whether the apparent low prevalence of ADHD and stimulant use disorder is an artefact of the data. It is difficult to interpret the rationale for stimulant prescription in this cohort, perhaps reflecting a lack of consistency, clarity and a regulatory framework for prescription of stimulants to people prescribed OAT. The study highlights the need to explore the role of stimulant co-prescription for both the treatment of stimulant use disorder and for the treatment of ADHD at the relevant doses for each of these conditions. New research from Norway exploring dexamfetamine prescription for amphetamine dependence among people on OAT may provide additional evidence [14] as will emerging evaluation data from Canadian safer supply measures that allow for prescription psychostimulants for the purposes of reducing harm from illegal stimulant use [15]. Context is outpacing evidence [3]. The manuscript of Young et al. [1] serves as a timely reminder of the need for consensus treatment guidelines for opioid use disorder that make recommendations for treatment of coexisting stimulant use disorder and other conditions such as ADHD. Nadine Ezard: Conceptualization (lead); project administration (lead); writing—original draft (lead); writing—review and editing (lead). Krista J. Siefried: Conceptualization (supporting); writing—original draft (supporting); writing—review and editing (supporting). Brendan Clifford: Conceptualization (supporting); writing—original draft (supporting); writing—review and editing (supporting). The authors have no financial interests to declare. The National Centre for Clinical Research on Emerging Drugs receives funding from the Australian Department of Health and Aged Care. Open access publishing facilitated by University of New South Wales, as part of the Wiley - University of New South Wales agreement via the Council of Australian University Librarians. Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score0.607

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 head0.030
GPT teacher head0.357
Teacher spread0.327 · 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 designNot applicable
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".

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Citations1
Published2025
Admission routes1
Has abstractyes

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