Commentary on Walsh <i>et al</i> . (2020): Tobacco and cannabis co‐use— considerations for treatment
Bibliographic record
Abstract
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".