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Record W2110147629 · doi:10.1093/ndt/16.suppl_7.41

Anaemia in chronic renal disease: lessons learned since Seville 1994

2001· review· en· W2110147629 on OpenAlexaff
Parfrey Ps

Bibliographic record

VenueNephrology Dialysis Transplantation · 2001
Typereview
Languageen
FieldMedicine
TopicErythropoietin and Anemia Treatment
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMedicineDiseaseKidney diseaseErythropoietinDialysisPopulationIntensive care medicineRenal functionInternal medicineClinical trialAnemiaHeart failureCardiology

Abstract

fetched live from OpenAlex

Cardiovascular disease is the major cause of death among patients with end-stage renal disease, accounting for almost half of all fatalities. In recent years much progress has been made in understanding the pathogenesis of cardiovascular disease in the uraemic population. Anaemia is a consistent finding in chronic renal disease, affecting up to 90% of patients, and the central role of anaemia in the development of cardiovascular dysfunction is now well established. A significant proportion of patients have established cardiovascular complications on initiation of dialysis, raising the possibility of early correction of anaemia as a strategy for preventing cardiovascular co-morbidities among renal patients. Randomized, controlled trials have shown that normalization of haemoglobin (Hb) with recombinant erythropoietin (rh-Epo) is of no cardiovascular benefit in haemodialysis patients with symptomatic heart failure, ischaemic heart disease, or severe left ventricular dilatation, although suggestive evidence exists for benefits at earlier stages of cardiac disease. Results from large-scale clinical trials are required to clarify the effects of early anaemia correction on mortality and cardiovascular function, as well as appropriate treatment targets in different patient populations. The potential exists for higher Hb levels to extend patient survival through cardioprotective 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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.980
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
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)0.0000.000

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 head0.046
GPT teacher head0.353
Teacher spread0.307 · 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 designOther design
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

Citations23
Published2001
Admission routes1
Has abstractyes

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