A Systematic Review of Complications Associated With Percutaneous Native Kidney Biopsies in Adults in Low- and Middle-Income Countries
Bibliographic record
Abstract
Introduction: Kidney biopsy is an important tool for making diagnoses and for assessing the drug treatment requirements and disease prognosis in the management of kidney diseases.There are variations in the rate of complications associated with kidney biopsies across countries, and this depends on various clinical and technical factors.The aim of this study is to report on complications associated with kidney biopsy performed in low-and middle-income countries.Methods: Two reviewers searched studies in MEDLINE, Embase, Cochrane Reviews, and African Journals Online.A random effects meta-analysis method was used to pool estimates of complications.Results: We identified 39 studies reporting on 19,500 kidney biopsies with overall complications (major þ minor) rate of 14.9% (95% confidence interval ¼ 11.4%-18.7%).Fewer complications were reported in biopsies performed with real-time ultrasound scans compared to those pre-marked using ultrasound or blind procedures (12.4% vs. 14.9% vs. 24.5%;P ¼ 0.037), respectively.Complications, albeit lower for procedures performed with automated needles (13.3%), were not significantly different from those performed with nonautomated needles (17.3%;P ¼ 0.588).Major complications included macroscopic hematuria (1.48%), nephrectomy (0.04%), blood loss requiring red cell transfusion (0.24%), angiographic intervention (0.22%), and death (0.01%).Conclusion: Complications associated with kidney biopsy in low-and middle-income countries are low, are comparable to those in other settings, and occur more sparingly when real-time ultrasound techniques or automated kidney biopsy needles are used.This suggests the need to expand the use of this procedure to improve diagnosis of kidney pathologies and choice of therapy when indicated.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".