Medical nutritional therapy for patients with gout and hyperuricemia: a systemic review
Bibliographic record
Abstract
Objective To evaluate the dietary risk factors and rational nutritonal intervention for gout and hyperuricemia. Methods PubMed, EMBASE, OVID, Cochrane Library, China National Knowledge Infrastructure (CNKI), and Chinese Wanfang Database were searched for literature related to dietary risk factors and medical nutritonal intervention for gout/hyperuricemia from January 2000 to December 2015. Strict screening of the searched literature was performed based on inclusion and exclusion criteria. The bias risk evaluation tool recommended by the Cochrane Handbook and the Newcastle-Ottawa Scale (NOS) scale were used to assess the quality of included literature. With odds ratios (OR) of gout/hyperuricemia for subjects with different dietary preferences as the main effect indicator, a meta-analysis was conducted, with data collected from literature and summarized by RevMan 5.1 software. Results Nine studies were included eventually. Compared with subjects with lowest intake of red meat, seafood, alcohol, sweented soft drinks or natural juice, the OR for gout/hyperuricemia among subjects with highest intake of these foods was 1.39 (95% CI: 1.18-1.63, P<0.000 1), 1.59 (95% CI: 1.33-1.90, P<0.000 01), 3.14 (95% CI: 2.17-4.53, P<0.000 01), 1.69 (95% CI: 1.17-2.43, P=0.005), and 1.46 (95% CI: 1.18-1.80, P=0.000 5), respectively. In contrast, compared with subjects with lowest intake of dairy products, vegitables (containing purine-rich vegetables) or coffee, the OR for gout/hyperuricemia among subjects with highest intake of these foods was 0.50 (95% CI: 0.40-0.64, P<0.00001), 1.01 (95% CI: 0.85-1.19, P=0.95), 0.48 (95% CI: 0.30-0.78, P=0.003), respectively. Conclusions Patients with gout or hyperuricemia should avoid or restrict the intake of high-purine animal foods (especially red meat and seafood), alcohol, high fructose or corn syrup-sweetened foods, while abstinence of purine-rich vegetables is not required. Regular intake of dairy products and coffee is recommended for such patients. Key words: Gout; Hyperuricemia; Medical nutritional therapy; Diet; Risk factors
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".