Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".