Association between Chronic Pain and Diabetes/Prediabetes: A Population-Based Cross-Sectional Survey in Saudi Arabia
Bibliographic record
Abstract
Background. Diabetes is a debilitating chronic health condition that is associated with certain pain syndromes. The present study sought to evaluate chronic pain and its association with diabetes mellitus at a population level. Methods. A population-based cross-sectional questionnaire survey study was conducted in Al-Kharj, Saudi Arabia, from January 2016 to June 2016. Participants from both private and governmental institutions were selected following a multistage sampling technique and using a cluster sampling method. Anthropometric measurements were taken, including body weight, height, body mass index (BMI) and waist circumference. A blood sample was also drawn from each respondent for fasting blood sugar, HbA1c, and fasting lipid profile. A P value of less than 0.05 indicated statistical significance. Results. A total of 1003 subjects were included for final analysis. Compared to prediabetic and nondiabetic individuals, diabetic subjects had a higher prevalence of lower limb pain (11.1%), back pain (8.9%), abdominal pain (6.7%), and neck pain (4.4%) (X2 = 27.792, P=0.015 ). In a multiple logistic regression model, after adjusting for age, gender, education level, cholesterol, and smoking status, diabetic/prediabetic patients had a significantly higher prevalence of chronic pain ((OR) = 1.931 (95% CI = 1.536–2.362), P=0.037 ). Increased age was also significantly associated with chronic pain ((OR) = 1.032 (95% CI = 1.010–1.054, P=0.004 ). Conclusion. Results of this study found a significant association between diabetes and prediabetes and chronic pain symptoms. Prospective studies are needed to explore temporality of such association.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".