Screening of Prediabetes and Type 2 Diabetes Mellitus in Rabigh, Saudi Arabia
Bibliographic record
Abstract
INTRODUCTION: Identifying people with an increasing risk of diabetes provides a chance to change some factors before the occurrence of serious sequelae and permits the expectation of diabetes tendencies and the necessitated means to manage emerging diabetes. This current research aimed to screen students versus employees in Rabigh campus, King Abdulaziz University for the incidence of prediabetes and diabetes mellitus Type 2.METHODS: A sample of 279 was proportionally taken from student and employee study groups. Structured Modified Diabetic Risk Test (MDRT) questionnaire was adapted to be filled by each participant. Impaired glucose tolerance, body mass index, waist circumference and blood pressure of all participants were quantified. This study was done from January 2017 to March 2017.RESULTS: Higher pre-diabetic and diabetic risks were observed in employee as compared to students (ORPre-Dia=4.07 with 95% CI =1.518-10.95; ORDia=2.913 with 95% CI 0.815-10.41). Waist circumference and body mass index of students showed significant association with glucose level with p values 0.003 and 0.002 respectively.CONCLUSION: There are an alarming number of individuals with the risk of being affected by diabetes in both students and employees among this study population. These findings emphasize on the need for a primary healthcare clinic role in the screening, management, follow up and promoting community awareness of Diabetes Mellitus.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".