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Enregistrement W2972030690 · doi:10.1111/add.14801

Reducing the risks of distortion in cannabis research

2019· article· en· W2972030690 sur OpenAlexaboutno aff
Keith Humphreys, Wayne Hall

Notice bibliographique

RevueAddiction · 2019
Typearticle
Langueen
DomaineMedicine
ThématiqueCannabis and Cannabinoid Research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésCannabisDistortion (music)Marijuana smokingPsychologyMedicineEnvironmental healthPsychiatryComputer scienceSubstance use

Résumé

récupéré en direct d'OpenAlex

Corporate and ideological vested interests may distort the findings of cannabis research. Scientists working in this area need to be aware of and actively manage these risks. We focus on one specific claim—that medical cannabis programs have reduced opioid overdose deaths in the United States—but similar lessons can be drawn from the way that weak evidence has been used to support claims that cannabis (whether putatively medical and/or explicitly recreational) can treat diabetes, psychiatric disorders, problem drinking, cancers and obesity, improve fetal health and prevent Alzheimer's disease, car accidents and suicide 1. Five years ago, a study in JAMA Internal Medicine reported that US states with medical cannabis programs had a slower than expected rate of increase in opioid overdose deaths between 1997 and 2010 relative to states without these programs 2. The cannabis industry claimed that the study proved that medical cannabis use had reduced opioid overdose deaths. The medical and recreational marijuana promoting company Weedmaps used billboards to advertise the claim 3, and many journalists chimed in with stories declaring that ‘Medical pot is our best hope to fight the opioid epidemic’ 4. Addiction's editors (including the two authors of this editorial) pointed out that correlations between state-level overdose death data and whether or not states had a medical cannabis program should be not interpreted as evidence that individuals could reduce their opioid overdose risk by using cannabis 5. We also highlighted other peer-reviewed studies that provided stronger evidence that was inconsistent with this claim (see 6-9). A recent study 10 vindicated the Addiction editorial. It extended the original study of marijuana law and opioid overdoses 2, using the same methods but adding seven more years of data (2011–17). The study found that the association between medical cannabis programs and opioid overdose mortality reversed over time, such that greater cannabis access was associated with higher opioid overdose death rates 10. Neither the cannabis industry nor its ideological allies have withdrawn their claims in light of these findings. For example, in an opinion piece apparently aimed at legislators 11, a representative of the US National Organization for the Reform of Marijuana Laws (NORML) criticized the methods of the recent study 10 while citing a limited selection of studies and praising the positive findings of studies that used similar designs. The pushback against the study's findings suggests some lessons for cannabis researchers to consider. In a number of countries, cannabis use has traditionally been associated with left-wing grassroots activism, making it easy to assume that the cannabis industry is run by altruistic hippies disinterestedly proselytizing the virtues of cannabis and unconcerned about worldly wealth 12. In the United States and Canada the industry is, in fact, run by business executives with law degrees and MBAs who are adopting the business practices of the tobacco and alcohol industries that are now major investors in large cannabis companies 12. Researchers should therefore not be surprised that the profit-maximizing cannabis industry highlights any ‘good news’ on cannabis and seeks to discredit any evidence of harm. Researchers who take funds from the cannabis industry should therefore be aware that accepting cannabis industry funding may come with strings, and could also soon have the same adverse reputational effects among one's colleagues as taking tobacco and alcohol industry funding. We believe that journals in our field should follow Addiction's practice in requiring researchers to disclose cannabis industry support in the same way as they disclose any support from the alcohol, tobacco, gambling and pharmaceutical industries. Journals should also call out misbehavior by the emerging cannabis industry as they do that of other industries producing and selling addictive products. Advocates for more lax, pro-corporate cannabis policies may use evidential double standards to portray the risks and benefits of cannabis use. Studies showing apparent harmful effects of cannabis use may be discounted for methodological reasons (e.g. confounding, lack of randomization), while studies with the same or worse methodological problems may be promoted because they provide more congenial results. The two studies of medical cannabis and opioid overdose illustrate this well, given that the second used the same methods as the first and had more data, yet was portrayed as a less reliable source of evidence. Journals place constraints on authors to prevent them overstating the implications of their findings, but the media is extremely interested in scientists who make controversial statements or who overstate the definitiveness of their findings. Researchers who succumb to these temptations are often rewarded by more media attention, speaking invitations and a more prominent profile that is often welcomed as a marker of public impact by their employers. Media training—including a component on ethics—could be a useful way to ensure more accurate press releases about the research of their employees 13. Critics may perhaps accuse us of setting unrealistically high scientific standards in reporting research on a largely ‘harmless’ drug that is claimed to have extraordinary medical benefits; but the lives of vulnerable people may be put at risk when science is distorted for corporate or ideological ends. As noted, several US states responded to the much-hyped original study of cannabis and opioid overdoses by authorizing the use of medical cannabis to treat heroin-addicted individuals. Advice that opioid users should use medical cannabis to replace opioid agonist therapies (e.g. methadone, buprenorphine) poses a significant risk because abrupt cessation of these medications dramatically increases the risks of an overdose death if users return to opioid use 14. Moreover, US states with high rates of opioid overdose deaths may be happier to allow patients to use medical cannabis to treat their opioid dependence (at their own expense) than to publicly fund treatment programs that have been shown to substantially reduce opioid overdose deaths; namely, opioid agonist therapies 15. Avoiding the distortion of science is not an academic nicety, but a solemn ethical responsibility that protects vulnerable human beings. We urge our colleagues to tread warily in this contested and volatile arena. K.H. was supported by grants from the Veterans Health Administration and the Wu Tsai Neurosciences Institute. This editorial expresses the authors’ perspectives and does not necessarily represent the views of their employers.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,663
Score d'incertitude au seuil0,940

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,071
Tête enseignante GPT0,397
Écart entre enseignants0,326 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations7
Publié2019
Routes d'admission1
Résumé présentoui

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