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Record W2596605364 · doi:10.1097/mnh.0000000000000317

Anemia management in chronic kidney disease and dialysis

2017· review· en· W2596605364 on OpenAlexaff
David Collister, Claudio Rigatto, Navdeep Tangri

Bibliographic record

VenueCurrent Opinion in Nephrology & Hypertension · 2017
Typereview
Languageen
FieldMedicine
TopicErythropoietin and Anemia Treatment
Canadian institutionsSeven Oaks General HospitalUniversity of ManitobaMcMaster University
Fundersnot available
KeywordsKidney diseaseMedicineAnemiaDialysisIntensive care medicineDiseaseInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: This review describes the current state of anemia management with erythropoietin (EPO)-stimulating agents and iron supplementation in both chronic kidney disease and dialysis patients, with a focus on novel therapies. RECENT FINDINGS: We review the benefits and risks of EPO-stimulating agents, focusing on health-related quality of life and the uncertainties regarding optimal iron utilization in patients with kidney disease. We discuss novel therapies for iron supplementation including iron-based phosphate binders and dialysate iron delivery as well as alternatives to EPO-stimulating agents including hypoxia-inducible factor prolyl hydroxylase inhibitors. SUMMARY: Individualization of hemoglobin targets using EPO-stimulating agents and iron supplementation may be considered in younger, healthier patients with kidney disease to improve health-related quality of life. Optimal iron utilization in kidney disease patients is unclear, but novel iron base phosphate binders and dialysate iron delivery may play a role in intravenous iron avoidance and its potential complications. Phase 3 randomized controlled trials of hypoxia-inducible factor prolyl hydroxylase inhibitors are ongoing and are promising new alternatives to EPO-stimulating agents and their known adverse effects.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.948
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
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 head0.118
GPT teacher head0.394
Teacher spread0.276 · 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

Citations33
Published2017
Admission routes1
Has abstractyes

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