Comparison of diabetes risk estimate in the cities of Riyadh and Amman
Bibliographic record
Abstract
A significant rise in the prevalence of type 2 diabetes mellitus (T2DM) in the Middle-east and North Africa (MENA) region has seen over the last few decades. The present observational study aimed to evaluate and compare the risk of developing T2DM in the cities of Riyadh and Amman using the Arab Diabetes Risk Assessment Questionnaire (ARABRISK).The ARABRISK was administered in a total of 1116 healthy male and female individuals in the age group of 40 to 74 years with no prior history of diabetes in the city of Riyadh (Saudi Arabia) and Amman (Jordan). ARABRISK is an Arabic version of the Canadian Diabetes Risk Assessment Questionnaire (CANRISK), which was adapted and validated for the use in Arab-speaking individuals in Saudi Arabia and Jordan.The participants from Amman region had higher mean total ARABRISK score compared to the Riyadh region for all categories of ARABRISK. However, the difference was significant in both low- and high-risk categories (P = .02 and P = .01, respectively) but not significant for moderate category (P = .17). In the Riyadh population, female participants had significantly higher ARABRISK total scores compared to male in both moderate- and high-risk categories (P = .01). However, in the Amman population, male participants had significantly higher ARABRISK total scores compared to female in both low- and moderate-risk categories (P = .01).The present study suggested an increased risk of developing T2DM in the cities of Riyadh and Amman. However, the population of Amman had a higher risk of developing T2DM compared to the population of Riyadh.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".