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Record W3097597876 · doi:10.1016/j.ekir.2020.10.019

A Systematic Review of Complications Associated With Percutaneous Native Kidney Biopsies in Adults in Low- and Middle-Income Countries

2020· review· en· W3097597876 on OpenAlexaff
Shepherd Kajawo, Udeme E. Ekrikpo, Mothusi Walter Moloi, Jean Jacques Noubiap, Mohamed A. Osman, Ugochi S. Okpechi‐Samuel, André Pascal Kengne, Aminu K. Bello, Ikechi G. Okpechi

Bibliographic record

VenueKidney International Reports · 2020
Typereview
Languageen
FieldMedicine
TopicRenal Diseases and Glomerulopathies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineBiopsyNephrectomyKidneyPercutaneousKidney diseaseConfidence intervalMEDLINESurgeryRadiologyInternal medicine

Abstract

fetched live from OpenAlex

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: tudies in MEDLINE, Embase, Cochrane Reviews, and African Journals Online. A random effects meta-analysis method was used to pool estimates of complications. RESULTS: = 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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.015
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.015
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.295
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations31
Published2020
Admission routes1
Has abstractyes

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