Bibliographic record
Abstract
目的 系统评价尿酸与肾脏疾病发生和预后的关系，为防治肾脏疾病提供参考。方法 检索中国生物医学文献数据库（CBM）（1982年1月—2010年3月）、荷兰医学文摘（EMBASE）（1966年1月—2010年3月）以及美国医学索引（Medline）（1950年1月—2010年3月），筛选尿酸与肾脏疾病发生和预后的相关文献，只纳入队列研究。根据Newcastle-Ottawa质量评价量表评价纳入研究的偏倚风险，2名研究人员独立筛选、评价文献和收集数据。采用Stata10.0软件进行统计学分析。结果 共纳入21篇高质量队列研究文章，其中与肾脏疾病发生相关11篇，共276801例研究对象；与肾脏疾病预后相关10篇，共3004例研究对象。对纳入研究中校正了除尿酸外其他影响肾脏疾病发生和预后因素的数据进行Meta分析，结果显示：（1）尿酸与肾脏疾病发生：尿酸水平升高会增加肾脏疾病的发生风险（RR=1.49，95％CI1.27～1.75）。（2）尿酸与肾脏疾病预后：高尿酸可导致肾功能恶化（RR=1.35，95％CI1.12~1.63）和肾脏疾病患者死亡风险增加（RR=1.67，95％CI1.29～2.16）。结论 尿酸是肾脏疾病发生和已患肾脏疾病患者预后不佳的独立危险因素。今后需开展高质量、长随访的临床试验，明确降低尿酸是否能降低肾脏疾病的发生风险和改善肾脏疾病患者的预后，为进一步明确尿酸与肾脏疾病的关系和高尿酸患者的防治提供直接依据。
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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 | 0.032 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.022 | 0.022 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.031 | 0.008 |
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".