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Enregistrement W2707721172 · doi:10.2105/ajph.2017.303818

Lower-Risk Cannabis Use Guidelines: A Comprehensive Update of Evidence and Recommendations

2017· review· en· W2707721172 sur OpenAlexafffundabout
Benedikt Fischer, Cayley Russell, Pamela Sabioni, Wim van den Brink, Bernard Le Foll, Wayne Hall, Jürgen Rehm, Robin Room

Notice bibliographique

RevueAmerican Journal of Public Health · 2017
Typereview
Langueen
DomaineMedicine
ThématiqueCannabis and Cannabinoid Research
Établissements canadiensCentre for Addiction and Mental Health
Organismes subventionnairesCanadian Institutes of Health Research
Mots-clésPsycINFOCannabisMedicineMEDLINEPublic healthCochrane LibrarySystematic reviewPsychological interventionFamily medicineEffects of cannabisEvidence-based medicinePsychiatryEnvironmental healthAlternative medicineNursing

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Cannabis use is common in North America, especially among young people, and is associated with a risk of various acute and chronic adverse health outcomes. Cannabis control regimes are evolving, for example toward a national legalization policy in Canada, with the aim to improve public health, and thus require evidence-based interventions. As cannabis-related health outcomes may be influenced by behaviors that are modifiable by the user, evidence-based Lower-Risk Cannabis Use Guidelines (LRCUG)-akin to similar guidelines in other health fields-offer a valuable, targeted prevention tool to improve public health outcomes. OBJECTIVES: To systematically review, update, and quality-grade evidence on behavioral factors determining adverse health outcomes from cannabis that may be modifiable by the user, and translate this evidence into revised LRCUG as a public health intervention tool based on an expert consensus process. SEARCH METHODS: We used pertinent medical search terms and structured search strategies, to search MEDLINE, EMBASE, PsycINFO, Cochrane Library databases, and reference lists primarily for systematic reviews and meta-analyses, and additional evidence on modifiable risk factors for adverse health outcomes from cannabis use. SELECTION CRITERIA: We included studies if they focused on potentially modifiable behavior-based factors for risks or harms for health from cannabis use, and excluded studies if cannabis use was assessed for therapeutic purposes. DATA COLLECTION AND ANALYSIS: We screened the titles and abstracts of all studies identified by the search strategy and assessed the full texts of all potentially eligible studies for inclusion; 2 of the authors independently extracted the data of all studies included in this review. We created Preferred Reporting Items for Systematic Reviews and Meta-Analyses flow-charts for each of the topical searches. Subsequently, we summarized the evidence by behavioral factor topic, quality-graded it by following standard (Grading of Recommendations Assessment, Development, and Evaluation; GRADE) criteria, and translated it into the LRCUG recommendations by the author expert collective on the basis of an iterative consensus process. MAIN RESULTS: For most recommendations, there was at least "substantial" (i.e., good-quality) evidence. We developed 10 major recommendations for lower-risk use: (1) the most effective way to avoid cannabis use-related health risks is abstinence, (2) avoid early age initiation of cannabis use (i.e., definitively before the age of 16 years), (3) choose low-potency tetrahydrocannabinol (THC) or balanced THC-to-cannabidiol (CBD)-ratio cannabis products, (4) abstain from using synthetic cannabinoids, (5) avoid combusted cannabis inhalation and give preference to nonsmoking use methods, (6) avoid deep or other risky inhalation practices, (7) avoid high-frequency (e.g., daily or near-daily) cannabis use, (8) abstain from cannabis-impaired driving, (9) populations at higher risk for cannabis use-related health problems should avoid use altogether, and (10) avoid combining previously mentioned risk behaviors (e.g., early initiation and high-frequency use). AUTHORS' CONCLUSIONS: Evidence indicates that a substantial extent of the risk of adverse health outcomes from cannabis use may be reduced by informed behavioral choices among users. The evidence-based LRCUG serve as a population-level education and intervention tool to inform such user choices toward improved public health outcomes. However, the LRCUG ought to be systematically communicated and supported by key regulation measures (e.g., cannabis product labeling, content regulation) to be effective. All of these measures are concretely possible under emerging legalization regimes, and should be actively implemented by regulatory authorities. The population-level impact of the LRCUG toward reducing cannabis use-related health risks should be evaluated. Public health implications. Cannabis control regimes are evolving, including legalization in North America, with uncertain impacts on public health. Evidence-based LRCUG offer a potentially valuable population-level tool to reduce the risk of adverse health outcomes from cannabis use among (especially young) users in legalization contexts, and hence to contribute to improved public health outcomes.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,037
score de la tête « metaresearch » (Gemma)0,139
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Revue systématique · Signal consensuel: Revue systématique
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,037
Score d'incertitude au seuil0,195

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0370,139
Méta-épidémiologie (sens strict)0,0020,002
Méta-épidémiologie (sens large)0,0080,010
Bibliométrie0,0220,014
Études des sciences et des technologies0,0010,002
Communication savante0,0050,007
Science ouverte0,0100,004
Intégrité de la recherche0,0080,006
Charge utile insuffisante (le modèle a refusé de juger)0,0100,003

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 Opus0,521
Tête enseignante GPT0,540
Écart entre enseignants0,020 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

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

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

En bref

Citations435
Publié2017
Routes d'admission3
Résumé présentoui

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