MétaCan
Menu
Back to cohort
Record W4406659171 · doi:10.1016/j.kint.2025.01.008

The role of sex and gender in acute kidney injury—consensus statements from the 33rd Acute Disease Quality Initiative

2025· article· en· W4406659171 on OpenAlexaff
Danielle E. Soranno, Linda Awdishu, Sean M. Bagshaw, David P. Basile, Samira Bell, Azra Bihorac, Joseph V. Bonventre, Alessandra Brendolan, Rolando Claure‐Del Granado, David Collister, Lisa M. Curtis, Kristin Dolan, Dana Y. Fuhrman, Zahraa Habeeb, Michael P. Hutchens, Kianoush Kashani, Nuttha Lumlertgul, Mignon McCulloch, Shina Menon, Amira Mohamed, Neesh Pannu, Karen Reue, Claudio Ronco, Manisha Sahay, Emily See, Michael Zappitelli, Ravindra L. Mehta, Marlies Ostermann

Bibliographic record

VenueKidney International · 2025
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare cost, quality, practices
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of TorontoUniversity of AlbertaAlberta Health Services
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesSchool of Medicine, Indiana University
KeywordsAcute kidney injuryMedicineConsensus conferenceDiseaseKidney diseaseIntensive care medicineFamily medicineInternal medicine

Abstract

fetched live from OpenAlex

Sex differences exist in acute kidney injury (AKI), and the role that sex and gender play along the AKI care continuum remains unclear. The 33rd Acute Disease Quality Initiative meeting evaluated available data on the role of sex and gender in AKI and identified knowledge gaps. Data from experimental models, pathophysiology, epidemiology, clinical care, gender, social determinants of health, education, and advocacy were reviewed. Recommendations include incorporating sex and gender into research along the bench-to-bedside spectrum; analyzing sex-stratified results; evaluating the effects of sex chromosomes, hormones, and gender on outcomes; considering fluctuations of hormone levels; studying the impact gender may have on access to care; and developing educational tools to inform patients, providers, and stakeholders. This meeting report summarizes what is known about sex and gender along the AKI care continuum and proposes an agenda for translational discovery to elucidate the role of sex and gender in AKI across the lifespan.

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)0.003
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.216
Threshold uncertainty score0.943

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.359
GPT teacher head0.571
Teacher spread0.213 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations24
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueKidney InternationalSame topicHealthcare cost, quality, practicesFrench-language works237,207