9 HYPERURICEMIA AND RISK OF HYPERTENSION RESISTANT TO THERAPY: A SYSTEMATIC REVIEW AND META-ANALYSIS
Bibliographic record
Abstract
Background: Hypertension is a common public health problem throughout the world. One of the most common risk factors of hypertension is hyperuricemia. However, there are limited data for the effect of hyperuricemia in hypertension undergoing therapy. The objective of this study is to discover the relationship between hyperuricemia and the risk for resistant hypertension. Methods: Systematic literature search was conducted based on the PRISMA flow diagram, from PubMed, PubMed Central, Cochrane, and Google Scholar with keywords. “hyperuricemia”, “uncontrolled hypertension”, “resistant hypertension”, and “hypertension”. Data extraction was done and Newcastle-Ottawa Scale was used to assess the study quality. The data was then analyzed using Revman 5.4.1 (Cochrane). Results: Four selected cohort studies with a total of 8247 patients. All of the included studies have good quality. From the data analysis, we found that hyperuricemia increased the odds of hypertension becoming resistant to therapy (OR: 2.29 [95% CI 1.26, 4.14]; p: 0.006). Forest plot showed the data was evenly distributed. Conclusion: Hyperuricemia increases the risk of hypertension being resistant to therapy. Therefore, uric acid levels should be controlled in hypertensive patients to optimize the effect of hypertension therapy.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.032 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".