Neck Circumference, a Novel Indicator for Hyperuricemia
Bibliographic record
Abstract
Background: Waist circumference has been correlated with the risk of hyperuricemia. Whether neck circumference is also associated with hyperuricemia has not been assessed. This study aimed to investigate whether neck circumference is associated with hyperuricemia. Methods: This study population from Beijing is part of the larger China-wide Risk Evaluation of Cancers in Chinese Diabetic Individuals: a lONgitudinal (REACTION) study. For this Beijing sub-center cross-sectional study, a total of 8971 subjects were recruited. Gender-specific multivariable-adjusted regression analyses were conducted to analyze the association of neck circumference and waist circumference with hyperuricemia and the association of neck circumference with serum uric acid levels in the non-hyperuricemia population. Results: After adjusting for confounding variables, regression analyses showed that neck circumference was positively associated with hyperuricemia (OR, 2.61 (1.86-3.67) for males and 3.27 (2.53-4.22) for females) in both genders; further, neck circumference was also positively associated with serum uric acid levels in non-hyperuricemia subjects (b, 2.58 (1.76-3.39) for males and 4.27 (3.70-4.84) for females) in both genders. Additionally, we demonstrated that neck circumference was similar to waist circumference in terms of the strength of association (OR, 3.03 for waist circumference vs. 2.61 for neck circumference in males, and 3.50 vs. 3.27 for females) with hyperuricemia and the ability to predict hyperuricemia (AUC, 0.63 for waist circumference vs. 0.61 for neck circumference in males, and 0.66 vs. 0.66 in females). Conclusion: Neck circumference is positively and independently associated with hyperuricemia in both genders and is also associated with serum uric acid levels in the non-hyperuricemia population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".