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Record W3017251349 · doi:10.1111/add.15043

Commentary on Walsh <i>et al</i> . (2020): Tobacco and cannabis co‐use— considerations for treatment

2020· letter· en· W3017251349 on OpenAlexafffundabout
Rachel A. Rabin

Bibliographic record

VenueAddiction · 2020
Typeletter
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsMcGill UniversityDouglas Mental Health University Institute
FundersCanada First Research Excellence FundMcGill University
KeywordsCannabisAbstinenceMedicinePsychiatryIntervention (counseling)Tobacco useSmoking cessationEnvironmental healthClinical psychologyPopulation

Abstract

fetched live from OpenAlex

Treatment success for tobacco and cannabis co-use remains poor. A better understanding of the triggers preceding relapse and when individuals are most vulnerable to them may guide clinicians towards adopting a single or multi-substance intervention approach. Treatment should consider high-risk groups who may be more resistant to abstinence maintenance. In the last decade there has been an upward trend in tobacco and cannabis co-use, which may reflect the changing legal landscape surrounding cannabis use [1], rendering this phenomenon a public health concern. Despite this, there is currently no gold standard for treating tobacco and cannabis co-use. Walsh et al. [2] performed a systematic review and meta-analysis investigating treatment efficacy for tobacco and/or cannabis use in intervention studies targeting co-users. Results demonstrate that current treatment strategies are far from satisfactory, only showing weak evidence for an effect on cannabis cessation and no clear effect on tobacco cessation. One limitation of the studies included in the meta-analysis is the under-representation of female participants, which is critical given the wealth of data supporting gender-specific effects associated with both tobacco and cannabis use. For example, several clinical reports have suggested that women are more vulnerable to tobacco [3] and cannabis [4] use compared to men, despite men being more sensitive to the rewarding effects of both drugs [5, 6]. In addition, women are less successful in their tobacco [7] and cannabis [8] quit attempts compared to men, which may reflect their greater severity of withdrawal symptoms [9, 10]. In light of these differences, considering gender-tailored pharmacological and behavioral interventions may lead to enhanced treatment success for both men and women co-users. Other specialized populations that warrant greater consideration for co-use treatment are individuals with serious mental illness, given that their rates of tobacco and cannabis use are two to three times higher than the general population [11, 12]. Among these patients, chronic use is associated with greater illness severity [13] and lower quit rate success [14]. Treating comorbid substance use presents additional challenges. First, tobacco and cannabis use may be more strongly associated in patients with severe mental illness [15] and secondly, addiction may be a direct consequence of the underlying neuropathophysiology of the psychiatric disorder [16], implying that unique treatment strategies may need to be considered for co-users with severe mental illness. Substance use disorders are chronic and relapsing in nature and thus prolonging abstinence is a primary focus of treatment. Determining critical windows for treating co-users (e.g. when vulnerability to relapse is at its highest) may help to inform clinicians whether single substance use interventions or multi-substance use interventions are more efficacious. One of the strongest predictors of relapse is the exposure to environmental stimuli that have become associated with the drug(s) of abuse. Paradoxically, cue-induced craving may not decrease linearly with abstinence over time, but may progressively increase or ‘incubate’ with longer periods of cessation in daily tobacco smokers [17]. While cue-induced craving for cannabis has been documented in problematic cannabis users [18], its trajectory during abstinence has not yet been established in human studies; however, pre-clinical studies suggest that an incubation effect may indeed exist [19]. Thus, tailoring interventions to coincide with periods of peak cue-induced craving, which may not necessarily occur when one first quits, or at the same time for tobacco and cannabis, may help to improve treatment efficacy for co-use. In summary, the high prevalence and negative consequences associated with tobacco and cannabis co-use underscore the need to identify empirically informed treatments. A better understanding of craving trajectories during abstinence and their association with relapse may help advocate for sequential or simultaneous treatment for co-use. Women and people with mental illness need to be studied alongside the general population as they represent subgroups of individuals who may be more resistant to cessation treatment and may require alternate intervention strategies. None. This work was undertaken thanks to funding from the Canada First Research Excellence Fund, awarded to the Healthy Brains for Healthy Lives initiative at McGill University.

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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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.017
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.032
GPT teacher head0.312
Teacher spread0.280 · 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.

Study designNot applicable
Domainnot available
GenreCommentary

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

Citations3
Published2020
Admission routes3
Has abstractyes

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