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Record W4387799189 · doi:10.1097/mcc.0000000000001091

Sex as a biological variable in acute kidney injury

2023· review· en· W4387799189 on OpenAlexaff
Anita Dahiya, Neesh Pannu, Danielle E. Soranno

Bibliographic record

VenueCurrent Opinion in Critical Care · 2023
Typereview
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineAcute kidney injuryIntensive care medicineEtiologySex characteristicsKidney diseaseDiseaseInternal medicineGuidelinePathology

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: The purpose of this review is to provide an overview of the preclinical and clinical studies investigating sex as a biological variable, as well as the impact of gender, on the development of and progression of acute kidney injury (AKI). RECENT FINDINGS: Despite a matched degree of ischemia-reperfusion AKI based on measured glomerular filtration rates, male and female mice demonstrated important sex biases in cardiorenal outcomes (1). Although the 2012 Kidney Disease Improving Global Outcomes (KDIGO) Clinical Practice Guideline for AKI reported that female sex is associated with increased rates of hospital acquired AKI, subsequent meta-analyses do not show increased risk of AKI in women. Recent large scale, multicenter epidemiologic studies suggest males have higher rates of hospital acquired AKI. However, women have been consistently shown to have worse renal outcomes after AKI. There may be also be gender-based differences in presentation to care and management. SUMMARY: Sex is an important biological variable in animal models of acute kidney injury. The impact of sex on AKI likely varies based on the etiology of AKI. Preclinical studies demonstrate the nuances of sex chromosomes, sex hormones and epigenetic factors on AKI, however these have not been well studied in humans. Gender may also impact processes of care, treatment and clinical outcomes related to AKI. The scientific rigor and reproducibility of translational studies benefit from the consideration of sex and gender.

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)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity, 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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.711
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.002

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 head0.396
GPT teacher head0.574
Teacher spread0.178 · 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

Citations14
Published2023
Admission routes1
Has abstractyes

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