A prognostic role for non-thyroidal illness syndrome in chronic renal failure:a systematic review and meta-analysis
Bibliographic record
Abstract
Background Chronic renal failure (CRF) is a serious disease that has become a burden on global and local economics and public health. In addition, non-thyroidal illness syndrome (NTIS) has become increasingly more prevalent in CRF patients. Materials and methods A data search was conducted on the PubMed/Medline, Cochrane Library, Web of Science, Embase, and CBM databases to identify studies up to November 1st, 2018, that compared low T3 and normal T3 levels in patients with CRF. Data analysis was done by calculating the relative risks (RR) and 95% confidence intervals (95% CI) and continuous variables were described by weighted mean difference (WMD) and 95% CI. The efficacy outcomes included renal function and mortality. The Newcastle-Ottawa Scale and Agency for Healthcare Research and Quality scale were used to assess the quality of the cohort and cross-sectional studies, respectively. A funnel plot was used to identify publication bias. Results Seventeen studies with a total of 4593 patients were finally included in the analysis. Among the 17 studies, 11 reported the mortality of CRF patients with low T3 and normal T3 levels. Subgroups were assigned according to different follow-up times and different methods of treatment. The mortality rate in the low T3 group was much higher than in the normal T3 group. 11 studies reported creatinine (Cr) results in patients with low T3 and normal T3 levels and our analysis found no significant differences between the two groups (95%CI: 0.46–0.25; P -heterogeneity = 0.000; P = 0.559). Five studies reported uric acid results and we found no significant differences between the two groups (95%CI: 0.08–0.22; P -heterogeneity = 0.438; P = 0.377). Five studies reported the urea levels in the two groups and our analysis found no significant differences (95%CI: 1.60–1.23; I 2 = 0.0%; P -heterogeneity = 0.498; P = 0.798). Conclusion Low T3 had a greater impact on the short-term prognosis of patients with CRF than on the long-term prognosis. NTIS did not cause substantial kidney damage.
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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.029 |
| Bibliometrics | 0.006 | 0.007 |
| 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) | 0.004 | 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".