Impact of renal impairment on outcomes of intracerebral hemorrhage: a systematic review and meta-analysis
Bibliographic record
Abstract
Chronic kidney disease (CKD) is linked to a number of cardiovascular complications, including intracerebral hemorrhage (ICH). However, the potential association between CKD and the outcomes of ICH are still inconclusive. This review aimed to clarify the relationship between CKD and ICH outcomes, such as mortality, functional disability, and length of hospitalization. A comprehensive literature search was done in MEDLINE, Scopus, and Cochrane Central Register of Controlled Trials (CENTRAL), Google Scholar, EMBASE databases from inception until November 2023. Observational studies examining adult ICH patients with CKD were included. Risk of bias was evaluated using Newcastle–Ottawa Scale, and data synthesis was performed by random-effects meta-analysis. Twenty studies were included, encompassing a broad range of CKD and ICH patients. The meta-analysis demonstrated a significant association between CKD and adverse ICH outcomes. CKD patients had significantly higher mortality rates (pooled OR = 2.21; 95%CI 1.88–2.59; I 2 = 98.7%) and functional disability (pooled OR = 1.51; 95%CI 1.30–1.75; I 2 = 97.2%). CKD also showed potential associations with increased duration of hospital stay, though this outcome was less consistently reported. Our findings showed that CKD was linked to higher rates of mortality, and functional disability in patients with ICH, and was associated with extended hospitalization. Our results underscore the need for a comprehensive, multidisciplinary approach to managing this complex patient population. Further research should elucidate the underlying mechanisms of this association and inform strategies for improving patient outcomes. The integration of nephrological and neurological care may significantly benefit the management and prognosis of ICH in patients with CKD.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.034 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".