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Record W2792673706 · doi:10.1093/ndt/gfx358

Women and kidney disease: reflections on World Kidney Day 2018

2018· editorial· en· W2792673706 on OpenAlexaff
Giorgina Barbara Piccoli, Mona Alrukhaimi, Elena Zakharova, Adeera Levin, Philip Kam‐Tao Li, Guillermo García-García, Mohammed Benghanem-Gharbi, Kamyar Kalantar‐Zadeh, Charles Kernahan, Latha Kumaraswami, Gamal Saadi, Louise Fox, Sharon Andreoli

Bibliographic record

VenueNephrology Dialysis Transplantation · 2018
Typeeditorial
Languageen
FieldMedicine
TopicPregnancy and Medication Impact
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineKidney diseaseDialysisPopulationDiseaseKidneyPregnancyIntensive care medicineInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

Chronic kidney disease (CKD) affects ∼10% of the world's adult population: it is one of the top 20 causes of death worldwide and its impact on patients and their families can be devastating. World Kidney Day and International Women's Day coincide in 2018, thus offering an opportunity to reflect on the importance of women's health, and specifically their kidney health, on the community and the next generations, as well as to strive to be more curious about the unique aspects of kidney disease in women so that we may apply these learnings more broadly. Girls and women, who make up ∼50% of the world's population, are important contributors to society and their families. Gender differences continue to exist around the world in access to education, medical care and participation in clinical studies. Pregnancy is a unique state for women, offering an opportunity for the diagnosis of kidney disease, and also a state where acute and chronic kidney diseases may manifest and that may impact future generations with respect to kidney health. There are various autoimmune and other conditions that are more likely to impact women with profound consequences for childbearing and on the fetus. Women have different complications on dialysis than men and are more likely to be donors than recipients of kidney transplants. In this editorial we focus on what we do and do not know about women, kidney health and kidney disease and what we might learn in the future to improve outcomes worldwide.

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), 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: Editorial · Consensus signal: Editorial
Teacher disagreement score0.059
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.309
Teacher spread0.293 · 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
GenreEditorial

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

Citations20
Published2018
Admission routes1
Has abstractyes

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