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Record W2142114662 · doi:10.2215/cjn.09960911

The New FDA Labeling for ESA—Implications for Patients and Providers

2012· article· en· W2142114662 on OpenAlexaff
Braden Manns, Marcello Tonelli

Bibliographic record

VenueClinical Journal of the American Society of Nephrology · 2012
Typearticle
Languageen
FieldMedicine
TopicErythropoietin and Anemia Treatment
Canadian institutionsLibin Cardiovascular Institute of AlbertaAlberta Kidney Disease NetworkUniversity of AlbertaUniversity of CalgaryAlberta Health Services
Fundersnot available
KeywordsMedicineErythropoiesisFood and drug administrationAnemiaIntensive care medicineDosingClinical trialKidney diseaseDrugHemoglobinDiseasePharmacologyInternal medicine

Abstract

fetched live from OpenAlex

Recent clinical trials comparing the use of erythropoiesis-stimulating agents targeting low (generally a hemoglobin of 90-115 g/L) and near-normal hemoglobin targets (generally a hemoglobin >130 g/L) in patients with chronic kidney disease have shown no improvements in clinical outcomes (aside from a small reduction in transfusion) and potential harm for erythropoiesis-stimulating agents use targeting near-normal hemoglobin targets. Based on these results, the US Food and Drug Administration recently released modified recommendations for more conservative dosing of erythropoiesis-stimulating agents in patients with CKD. These recommendations now stress individualizing therapy for each patient and using the lowest possible erythropoiesis-stimulating agents dose required to reduce the need for transfusions. The evolution in the management of anemia associated with chronic kidney disease over time and the recent evidence that has stimulated these labeling changes is discussed. Also, the US Food and Drug Administration labeling changes are discussed, and areas of controversy are highlighted. Although the US Food and Drug Administration labeling changes are based on the results of recent large randomized trials testing ESAs targeting near-normal hemoglobin levels, more specific guidance to clinicians would have been helpful.

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)0.000
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score0.335

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.054
GPT teacher head0.395
Teacher spread0.341 · 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
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

Citations44
Published2012
Admission routes1
Has abstractyes

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