Cannabis use and crash risk in drivers
Notice bibliographique
Résumé
In a recent paper published in Addiction, Rogeberg & Elvik 1 replicated and criticized two meta-analyses synthesizing the epidemiological evidence for marijuana use and crash risk in drivers 2, 3. The two meta-analyses were performed independently by two research teams (ours at Columbia University and Asbridge et al.'s [3] at Dalhousie University) and were published a few months apart in 2012 in two highly regarded academic journals. While we appreciate Rogeberg & Elvik's interest in our work, we feel it is incumbent upon us to respond to their criticisms of our study. Rogeberg & Elvik's critique of our study centers on three main issues: study selection, estimated effect size and data extraction. First, Rogeberg & Elvik state that our ‘selection criteria are unclear and hard to rationalize’. Study selection is an important step in any meta-analysis. In the Methods section, we stated explicitly that we followed ‘the guidelines put forth in the PRISMA statement and MOOSE guidelines for reporting systematic reviews and meta-analyses of observational studies in Epidemiology’ and spelled out the precise search terms as well as eligibility criteria, including a detailed 11-level graph illustrating the study selection process. We chose to cast a wide net in terms of exposure to cannabis use within studies, entering results as they were reported by the original investigators. We made clear that our estimated summary odds ratio was based on empirical data reported in the source studies and was not adjusted for any confounding factors. Albeit imperfect, our approach reflected accurately the state of the evidence as supported by the epidemiological literature. Secondly, Rogeberg & Elvik assert that the comparable odds ratios reported by us and Asbridge et al. overestimate substantially the true odds ratio of crashes ascribed to marijuana-induced impairment. This criticism, based on results of simulation exercises with sparse data and unsubstantiated assumptions, is unwarranted and misguided. The studies included in our meta-analysis assessed the associations of crash risk with marijuana use as indicated by drug testing or self-reporting, not assumed marijuana-induced impairment. Simulation models are no substitute for empirical evidence. Finally, Rogeberg & Elvik state that they found a data abstraction error regarding the Fergusson & Horwood study 4 in our meta-analysis, ‘where the correct OR was 1.4 rather than the 2.4 extracted by Li et al.’ There is no error in our data abstraction. The Fergusson & Horwood study 4 is the only study included in our meta-analysis that is based on a cohort design. Accordingly, the authors reported their results in rate ratios based on person-years. To include the data from this cohort study in our meta-analysis, we reconstructed the rate ratio data presented in Table 1 of the Fergusson & Horwood paper and converted them into odds ratios. This conversion is necessary, because other studies included in our meta-analysis were based on case–control and cross-sectional designs and reported their findings in odds ratios. Rogeberg & Elvik do not seem to understand the differences between these epidemiological study designs. It is apparent that they mistake the rate ratio reported by Fergusson & Horwood for odds ratio. None.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».