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Record W2147109841 · doi:10.1517/14712598.2015.971749

Erythropoiesis-stimulating agents in allogeneic and autologous hematopoietic stem cell transplantation

2014· review· en· W2147109841 on OpenAlexaff
Massimo Martino, Francesco Lanza, Taner Demirer, Tiziana Moscato, Simona Secondino, Paolo Pedrazzoli

Bibliographic record

VenueExpert Opinion on Biological Therapy · 2014
Typereview
Languageen
FieldMedicine
TopicErythropoietin and Anemia Treatment
Canadian institutionsUniversity Hospital Foundation
Fundersnot available
KeywordsMedicineStem cellExpert opinionIntensive care medicineHaematopoiesisHematopoietic stem cell transplantationErythropoiesisTransplantationAplastic anemiaClinical trialAnemiaOncologyImmunologyInternal medicineBone marrow

Abstract

fetched live from OpenAlex

INTRODUCTION: Erythropoiesis-stimulating agents (ESAs) are used in treating cancer- and chemotherapy-induced anemia with the aim of accelerating the recovery of red blood cells (RBCs), reduce the risks associated with RBC transfusions and improve quality of life. AREAS COVERED: A systematic review has been conducted to examine the current evidence for the efficacy and safety of using ESAs in hematopoietic stem cell transplants (HSCTs). EXPERT OPINION: Despite the international recommendations for the use of ESAs in treating different malignancies, there is a lack of guidelines for their use in patients undergoing HSCT. An evaluation of published clinical trials shows that there are no available powerful studies concerning the use of ESAs in this setting, with only heterogeneous and small numbers of patients reported so far. Nevertheless, the more robust and intriguing of these data suggest that the ESA's administration at an appropriate time after the infusion of stem cells may be effective both in autologous and allogeneic HSCTs. New guidelines are required, overseen by an expert in the in the field of stem cell transplantation.

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 score0.997
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.154
GPT teacher head0.401
Teacher spread0.247 · 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

Citations4
Published2014
Admission routes1
Has abstractyes

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