The relationship between chronic pain, prehypertension, and hypertension. A population-based cross-sectional survey in Al-Kharj, Saudi Arabia
Bibliographic record
Abstract
Background: Chronic pain and hypertension are highly prevalent in both developing and developed countries. Although they may appear to be two separate phenomena, several studies in developed countries have found them associated at the population level. Studies in developing countries are scarce and association between pain with prehypertension are rarely explored. The objective of this study was to explore the potential association between prehypertension, hypertension, and chronic pain in a Saudi population.Methods: A cross-sectional general population-based study was conducted on a random sample of employees and university students over a period of 6 months from January 2016 to June 2016 in Al Kharj, Saudi Arabia. A total of 1200 general population adults (aged 18 years and above) were invited to participate in the study.Results: With a response rate of 85.9%, 1031 individuals were included in the final analysis. Among the general population of Al-Kharj, statistically significant association was found between age and chronic pain [Odds ratio (OR) = 1.764 [95% C.I. = 1.391–1.927], P < 0.0001] and between hypertension and chronic pain [(OR) = 1.039 [95% C.I. = 1.018–1.060], P < 0.0001], respectively. The association between prehypertension and chronic pain was not statistically significant [(OR) = 1.211 [95% C.I. = 0.879–1.668, P = 0.243].Conclusion: Results of this survey suggests a statistically significant relationship between hypertension (but not prehypertension) and chronic pain. The temporality of the relationship between hypertension and chronic needs to be explored in future longitudinal studies.
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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 | 0.000 | 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".