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Record W2321777937 · doi:10.1159/000445091

Acute Kidney Injury in Western Countries

2016· review· en· W2321777937 on OpenAlexaff
Josée Bouchard, Ravindra L. Mehta

Bibliographic record

VenueKidney Diseases · 2016
Typereview
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsUniversité de Montréal
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesUniversity of California, San Diego
KeywordsAcute kidney injuryMedicineInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Acute kidney injury (AKI) is frequent and is associated with poor outcomes, including increased mortality, higher risk of chronic kidney disease, and prolonged hospital lengths of stay. The epidemiology of AKI mainly derives from studies performed in Western high-income countries. More limited data are available from Western low-income and middle-income countries (LMICs) located in Central and South America. SUMMARY: In this review, we summarize the most recent data on the epidemiology of AKI in Western countries, aiming to contrast results from industrialized high-income countries with LMICs. The global picture of AKI in LMICs is not as well characterized as in the USA and Europe. In addition, in some LMICs, the epidemiology of AKI may vary depending on the region and socioeconomic status, which contributes to the difficulty of getting a better portrait of the clinical condition. In low-income regions and tropical countries, AKI is frequently attributed to diarrhea, infections, nephrotoxins, as well as obstetric complications. As opposed to the situation in high-income countries, access to basic care in LMICs is limited by economic constraints, and treatment is often delayed due to late presentation and recognition of the condition, which contribute to worse outcomes. In addition, dialysis is often not available or must be paid by patients, which further restricts its use. KEY MESSAGES: There are great disparities in the epidemiology of AKI between Western high-income countries and Western LMICs. In LMICs, education and training programs should increase the public awareness of AKI and improve preventive and basic treatments to improve AKI outcomes. FACTS FROM EAST AND WEST: (1) More than 90% of the patients recruited in AKI studies using KDIGO-equivalent criteria originate from North America, Europe, or Oceania, although these regions represent less than a fifth of the global population. However, the pooled incidence of AKI in hospitalized patients reaches 20% globally with moderate variance between regions. (2) The lower incidence rates observed in Asian countries (except Japan) may be due to a poorer recognition rate, for instance because of less systematically performed serum creatinine tests. (3) AKI patients in South and Southeastern Asia are younger than in East Asia and Western countries and present with fewer comorbidities. (4) Asian countries (and to a certain extent Latin America) face specific challenges that lead to AKI: nephrotoxicity of traditional herbal and less strictly regulated nonprescription medicines, environmental toxins (snake, bee, and wasp venoms), and tropical infectious diseases (malaria and leptospirosis). A higher incidence and less efficient management of natural disasters (particularly earthquakes) are also causes of AKI that Western countries are less likely to encounter. (5) The incidence of obstetric AKI decreased globally together with an improvement in socioeconomic levels particularly in China and India in the last decades. However, antenatal care and abortion management must be improved to reduce AKI in women, particularly in rural areas. (6) Earlier nephrology referral and better access to peritoneal dialysis should improve the outcome of AKI patients.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.180
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.005

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 head0.032
GPT teacher head0.394
Teacher spread0.362 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations50
Published2016
Admission routes1
Has abstractyes

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