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Record W2012086596 · doi:10.1097/mnh.0b013e32835fe5c5

Bidirectional relationships between acute kidney injury and chronic kidney disease

2013· review· en· W2012086596 on OpenAlexaff
Neesh Pannu

Bibliographic record

VenueCurrent Opinion in Nephrology & Hypertension · 2013
Typereview
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsKidney diseaseAcute kidney injuryMedicineRenal functionIntensive care medicineInternal medicineDisease

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Chronic kidney disease (CKD) remains one of the most potent predictors of acute kidney injury (AKI); however, recent epidemiologic studies have demonstrated a complex interplay between these two clinical entities. A growing body of evidence supports a bidirectional relationship: AKI leads to CKD, and the presence of CKD increases the risk of AKI. Additionally, several studies suggest that the presence of underlying CKD does modify the relation between AKI and adverse outcomes. In this article, we will review recent studies supporting the hypothesis that AKI leads to CKD and will explore the role of CKD as an effect modifier for AKI. RECENT FINDINGS: A recent meta-analysis confirms the association between AKI and the development of CKD and end-stage renal disease. Patient survival and renal outcomes after AKI are influenced by the presence of underlying CKD. AKI survivors with complete recovery of renal function remain at elevated risk of developing de-novo CKD, which may influence long-term survival; however, recovery of kidney function after AKI is associated with better long-term survival and renal function. SUMMARY: Recent findings support a strong association between AKI and CKD. There is uncertainty as to whether this relationship is causal. CKD is an effect modifier in AKI.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.346
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.164
GPT teacher head0.411
Teacher spread0.247 · 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 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
Published2013
Admission routes1
Has abstractyes

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