Evaluation of Serum Interleukin-6 Levels in the Renal Transplant Recipients: A Systematic Review and Meta-Analysis of Case-Control Studies.
Bibliographic record
Abstract
AIM: The purpose of this meta-analysis was the assessment of the serum IL-6 levels in the renal transplant recipients compared to the healthy controls. MATERIAL AND METHODS: Four databases including PubMed, Web of Science, Scopus, and Cochrane Library were searched up to July 2018 without language restriction. The quality of studies was evaluated using the Newcastle-Ottawa scale (NOS). A continuous random-effects meta-analysis was used by RevMan 5.3 using the mean difference (MD) and 95% confidence intervals (CIs). Also, a regression model was done by Comprehensive Meta-Analysis version 2 (CMA v2). RESULTS: Out of 615 studies identified in the databases, 15 studies included and analysed in the meta-analysis. The studies were reported from 1994 to 2018. The meta-analysis included 1035 renal transplant recipients and 682 healthy controls. The pooled MD of the serum IL-6 levels in the transplant recipients compared to the healthy controls was 3.25 pg/mL [95%CI: 2.17, 4.32; P < 0.00001; I2 = 98% (P < 0.00001)]. Meta-regression analysis showed that one of the reasons of heterogeneity is the year of publication (Correlation coefficient (r) = 0.208, p-value = 0.00002). CONCLUSION: An elevated serum IL-6 level in the renal transplant recipients compared to the healthy controls showed that the serum level of this marker could be used for the evaluation of inflammation in ESRD patients undergoing renal transplantation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.047 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".