Notice bibliographique
Résumé
Dear Editor, Diabetes has become one of the major public health problems in Saudi Arabia, with an estimated prevalence of 18.5% among Saudi adults. A study conducted by Al-Rubeaan et al.[1] found that approximately 40% of diabetic patients in Saudi Arabia are unaware of their condition. The enormous globalization of the twentieth and twenty-first centuries has had a major impact on Saudi Arabia similar to other parts of the world with remarkable population migrations, cultural exchange, changes in dietary habits, and lifestyle. Similar to other parts of the world, adaptation of “modern” or “Western” lifestyles, with abundant, calorically dense foods and patterns of daily living that involve minimal physical activity, are temporally associated with increased rates of diabetes. Under these environmental stressors, the prevalence of diabetes in Saudi Arabia is expected to double by 2030.[2] The mortalities due to noncommunicable disease and their risk factors in Saudi Arabia and other countries (League of Arab States) are presented in Figure 1A and B, respectively. In all League of Arab States, the main cause of death was cardiovascular diseases. In Saudi Arabia, 145.32 (120.08–174.52) deaths per 100,000 reported due to cardiovascular diseases, followed by diabetes at 45.83 (35.98–56.78) deaths per 100,000, and neoplasms at 32.47 (26.14–40.48) deaths per 100,000. In the majority of countries, the main cause was found to be metabolic and behavioral [Figure 1B].Figure 1: Deaths due to noncommunicable diseases and risk in the League of Arab States. (A) Mortality rates by both sexes (male and female), all age groups per 100,000; (B) risk factors by both sexes (male and female), all age groups per 100,000. Source. Adopted from the Global Burden of Disease Compare 2021In Saudi Arabia, like other countries, there is a significant association between noncommunicable diseases and new cultures.[3] Addressing the staggering rise in diabetes among Saudi Arabians will require a strong and committed effort. The first steps include population awareness and timely diagnosis of affected persons. Promulgation and adaptation of lifestyle measures that have been successful in other countries is also something that could be adopted rapidly. Based on the work of Raj et al.,[4] significant reductions in glycated hemoglobin levels were observed in patients who adhered to dietary recommendations based on medical nutrition therapy as described by the Canadian Diabetes Association. In Saudi Arabia, dietary management should be considered a major part of diabetes treatment, and there is a need for separate evidence-based dietary guidelines for at-risk people. Furthermore, research specific to Saudi Arabians with diabetes is also needed. Large-scale population-based studies, clinical trials, and implementation studies are needed to understand the pathogenesis of disease in this population, determine preventive and treatment strategies, and minimize complications. Acknowledgement The author acknowledges feedback and suggestions from Jin Hui (Southeast University, China) and David A. D’Alessio (Duke University School of Medicine, USA). Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,008 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 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; un appel candidat d’une seule source (Gemma direct ou Codex distillé), 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 ».