Bibliographic record
Abstract
The global population is living amid a metabolic explosion. The prevalence of hyperuricemia, as a metabolic disorder and a causal agent of non-communicable diseases, has been gearing up rapidly worldwide during the last two decades due to consuming a high purine diet, alcohol, red meat, high fructose-containing food, and lifestyle changes. The invisible bond between hyperuricemia and many non-communicable diseases is more robust than before. During the evolution of hyperuricemia, systemic inflammation develops, leading to endothelial dysfunction and end-organ injury. These molecular changes were not recognized previously. Hyperuricemia is now a metabolic, more clearly a vascular disorder than a crystallization disease. Asymptomatic hyperuricemia is no more benign, and gout is not synonym with hyperuricemia or vice versa. Diagnose hyperuricemia in an early stage at a high normal level and control it to prevent the development and complications of many hyperuricemia-related extra-articular diseases. For more acceptance and importance, this high normal level of serum uric acid can be named prehyperuricemia. As in the case of prediabetes and prehypertension, prehyperuricemia should be diagnosed early irrespective of age and sex; preventive measures have to be taken and maintain uric acid at a safer level. keywords: Serum uric acid, hyperuricemia, prehyperuricemia, high normal value of serum uric acid, metabolic disorder, non-communicable disorder, and molecular mechanism.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".