Prognostic value of circulating uric acid in gastrointestinal cancers, a systematic review and meta-analysis.
Bibliographic record
Abstract
Aim: Thus, this meta-analysis was performed to assess the prognostic value of serum uric acid in patients with gastrointestinal cancers (GI). Background: There is growing evidence that high serum uric acid may be used as a potential prognostic marker in gastrointestinal malignancies. However, there are inconsistencies in the reported findings. Methods: Related studies were identified by searching the following databases: PubMed, Web of Science, Cochrane Library, and Scopus, independently up until 30 October 2023. Relevant analyses were carried out to deal with heterogeneity in the data. According to the inclusion criteria, we used English original papers reporting prognostic value of serum/plasma uric acid to determine hazard ratio (HR) and 95% confidence interval (CI) in patients with GI cancers. Pooled hazard ratios (HRs) with 95% confidence intervals (CIs) were used to ascertain the association of uric acid levels with gastrointestinal cancer (GI) risk. The inconsistency index (I2) was used to calculate the level of heterogeneity among the selected studies. The quality of each study was evaluated by Newcastle-OTTAWA Scales (NOS). Results: A total of 9 papers with 95.285 patients were included in this meta-analysis. The findings indicated a significant association between serum uric acid and poor prognosis in patients with gastrointestinal cancers (HR=1.477, 95% CI 1.165-1.873, P= 0.001). Further, in Subgroup analysis we found that patients would have poor survival rate among different cut-offs of uric acid, ≥ 5mg/dl, HR= 1.403, 95% CI=1.150-1.711, P=0.001 vs cut-off <5mg/dl, HR=1.54, 95% CI=1.140-2.063, P=0.005. Conclusion: Serum uric acid level is significantly linked to survival outcomes in patients with gastrointestinal cancers. Serum uric acid levels may be an effective prognostic marker associated with clinical outcomes in patients with gastrointestinal cancers. Given the small number of studies included in this meta-analysis and high heterogeneity, we suggest that a more comprehensive study is required to achieve more robust results.
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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 | 0.015 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.050 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".