Alcohol hangovers among male and female social drinkers: Do they differ?
Notice bibliographique
Résumé
Introduction: Men and women differ significantly in severity levels of acute alcohol intoxication symptoms, even after correcting for body weight, amount of alcohol consumed, and estimated peak blood alcohol concentration (eBAC). This study examined whether there are similar gender differences in the presence and severity of alcohol hangover symptoms.\nMethod: Survey data from N=2547 Dutch students (male = 44.5% female= 55.5%) was used to analyze possible gender differences in the presence and severity of 22 hangover symptoms, experienced on their past month heaviest drinking occasion. Symptoms were scored on an 11-point scale ranging from 0 (absent) to 10 (extreme). The analysis were conducted separately for different eBAC ranges, including < 0.08%, 0.08% - 0.11%, 0.11% – 0.20%, 0.20% – 0.30%, and 0.30% - 0.40%.\nResults: In the lowest (<0.08%) and highest (0.30% – 0.40%) eBAC range no significant gender differences were found. In the eBAC range 0.08% - 0.11%, significantly higher severity scores of nausea were reported by women. Most drinkers were allocated to the eBAC range of 0.11% to 0.20%. At this drinking level, women reported significantly higher severity scores on nausea, tiredness, weakness, and dizziness than men. Men reported significantly more often the presence of confusion, whereas women reported significantly more often the presence of shivering. In the eBAC range 0.20%-0.30% women reported higher severity of nausea and tiredness.\nDiscussions and Conclusions: During alcohol hangover, severity scores of nausea and tiredness were usually higher in women than men.\nImplications for practice: Although statistically significant gender differences were observed, these differences were of small magnitude (i.e. less than 1 on a scale of 0 to 10), and therefore have little clinical relevance.\nDisclosure of Interest Statement: This study was funded by Utrecht University. Joris Verster has received grants/research support from the Dutch Ministry of Infrastructure and the Environment, Janssen, Nutricia, Red Bull, Sequential, and Takeda, and has acted as a consultant for Canadian Beverage Association, Centraal Bureau Drogisterijbedrijven, Clinilabs, Coleman Frost, Danone, Deenox, Eisai, Janssen, Jazz, More Labs, Purdue, Red Bull, Sanofi-Aventis, Sen-Jam Pharmaceutical, Sepracor, Takeda, Toast!, Transcept, Trimbos Institute, Vital Beverages, and ZBiotics. The other authors have no potential conflicts of interest to disclose.
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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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,009 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,007 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,004 | 0,004 |
| Intégrité de la recherche | 0,004 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,003 |
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.
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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».