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Record W2237744571 · doi:10.12968/coan.2015.20.8.448

CPD article: Anaemia of renal disease: pathophysiology and treatment updates

2015· article· en· W2237744571 on OpenAlexaff
Serge Chalhoub, Cathy Langston

Bibliographic record

VenueCompanion animal · 2015
Typearticle
Languageen
FieldMedicine
TopicErythropoietin and Anemia Treatment
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineCATSKidney diseaseErythropoietinDiseaseDarbepoetin alfaChronic renal diseaseErythropoiesisIntensive care medicineAnemiaInternal medicine

Abstract

fetched live from OpenAlex

Chronic kidney disease (CKD) often leads to renal anaemia, due to gradual reduction of erythropoietin-producing renal cells. About 15–30% of geriatric cats develop CKD, with renal disease being the primary cause of death of older cats. Of those cats with CKD, up to 65% in later-stage CKD will develop renal anaemia. Recognising and treating anaemia of renal disease is an important part of CKD therapy in both dogs and cats. The use of erythropoiesis-stimulating agents (ESAs) is standard-of-care in humans and becoming more used in veterinary medicine. Darbepoetin alfa (DA) has been shown to be effective in the treatment of renal anaemia.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.386
Threshold uncertainty score0.348

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.036
GPT teacher head0.295
Teacher spread0.259 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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