Perioperative management for people with kidney failure receiving dialysis: A scoping review
Bibliographic record
Abstract
BACKGROUND: People with kidney failure receiving dialysis (CKD-G5D) are more likely to undergo surgery and experience poorer postoperative outcomes than those without kidney failure. In this scoping review, we aimed to systematically identify and summarize perioperative strategies, protocols, pathways, and interventions that have been studied or implemented for people with CKD-G5D. METHODS: We searched MEDLINE, EMBASE, CINAHL Plus, Cochrane Database of Systematic Reviews, and Cochrane Controlled Trials registry (inception to February 2020), in addition to an extensive grey literature search, for sources that reported on a perioperative strategy to guide management for people with CKD-G5D. We summarized the overall study characteristics and perioperative management strategies and identified evidence gaps based on surgery type and perioperative domain. Publication trends over time were assessed, stratified by surgery type and study design. RESULTS: We included 183 studies; the most common study design was a randomized controlled trial (27%), with 67% of publications focused on either kidney transplantation or dialysis vascular access. Transplant-related studies often focused on fluid and volume management strategies and risk stratification, whereas dialysis vascular access studies focused most often on imaging. The number of publications increased over time, across all surgery types, though driven by non-randomized study designs. CONCLUSIONS: Despite many current gaps in perioperative research for patients with CKD-G5D, evidence generation supporting perioperative management is increasing, with recent growth driven primarily by non-randomized studies. Our review may inform organization of evidence-based strategies into perioperative care pathways where evidence is available while also highlighting gaps that future perioperative research can address.
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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 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".