MétaCan
Menu
Back to cohort

Sex, haemoglobin and kidney disease: new perspectives

2005· review· en· W2144576762 on OpenAlexaff
John A. Duncan, Adeera Levin

Bibliographic record

VenueEuropean Journal of Clinical Investigation · 2005
Typereview
Languageen
FieldMedicine
TopicErythropoietin and Anemia Treatment
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsKidney diseaseMedicineDiseaseKidneyInternal medicinePhysiologyIntensive care medicine

Abstract

fetched live from OpenAlex

Anaemia is recognized as a common complication of chronic kidney disease with significant associated morbidity and mortality. Published data document the negative impact of anaemia on cardiovascular disease outcomes, progression of chronic kidney disease (CKD), hospitalizations, rehabilitation and quality of life, among others. Gender differences have been identified in many of these areas as well as, and importantly, in cardiovascular and chronic kidney disease outcomes. Female gender is associated with lower risk of cardiovascular morbidity and mortality as well as slower progression of chronic kidney disease. Interestingly, there are some well-described physiological adaptations to anaemia in women, which include shifting of the haemoglobin oxygen dissociation curve to reduce oxygen affinity secondary to higher levels of 2,3-diphosphoglycerate. However, the complex physiology underlying the impact of anaemia or gender on patients with chronic kidney disease is not well-characterized to date. Furthermore, there has been little examination of the potential interaction between anaemia and gender on cardiac or kidney outcomes. In this paper, we review the documented impact of anaemia and gender on outcomes, and explore the interaction of anaemia and gender in patients with CKD. We also present data that describe the potential importance of considering gender when targeting specific levels of haemoglobin. The value of a new perspective on haemoglobin and gender in kidney disease is important from both a physiological and an economical perspective.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.946
Threshold uncertainty score0.889

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.163
GPT teacher head0.431
Teacher spread0.268 · 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 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

Citations25
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueEuropean Journal of Clinical InvestigationSame topicErythropoietin and Anemia TreatmentFrench-language works237,207