Bibliographic record
Abstract
PURPOSE OF REVIEW: Gout is increasing worldwide. An appreciation that hyperuricaemia and gout are associated with hypertension and chronic kidney disease is well established, but the cause and effect relationships are controversial. Studies which address this conundrum have been reviewed. RECENT FINDINGS: Epidemiological surveys have confirmed the strong relationship of gout and hyperuricaemia with hypertension and diuretic treatment. There are multiple confounders such as obesity and alcohol consumption which despite adjustments make interpretation of the epidemiology difficult. There are data to suggest that hyperuricaemia itself causes hypertension and renovascular disease, and that lowering of serum urate may assist in control of hypertension. The mechanism for diuretic-induced hyperuricaemia may operate through volume depletion and reduced secretion of uric acid. The latter effect may be genetically influenced. SUMMARY: Recent population surveys have strongly supported the association of gout and hyperuricaemia with hypertension. The prevailing explanation that renal dysfunction causes both phenomena or that they are caused by shared factors is challenged by the evidence that hyperuricaemia drives hypertension. A confounder of epidemiology studies is the use of diuretics for treating hypertension. A closer understanding of the mechanisms of diuretic-induced hyperuricaemia may lead to the creation of uricosuric diuretics. Losartan is exceptional amongst antihypertensive drugs in possessing mild uricosuric properties and therefore has a role in treating hypertensive patients with gout. Overcoming diuretic-induced hyperuricaemia is difficult and there is need for a uricosuric diuretic.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".