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Record W2224014107 · doi:10.1080/10245332.2015.1101972

Erythropoietin in anemia of unknown etiology: A systematic review and meta-analysis

2016· review· en· W2224014107 on OpenAlexaff
Swetha Sriram, Anargyros Xenocostas, Alejandro Lazo‐Langner

Bibliographic record

VenueHematology · 2016
Typereview
Languageen
FieldMedicine
TopicErythropoietin and Anemia Treatment
Canadian institutionsWestern University
FundersWorld Health Organization
KeywordsAnemiaMedicineMeta-analysisErythropoietinEtiologyInternal medicineObservational studyHematologyHemoglobinGastroenterology

Abstract

fetched live from OpenAlex

INTRODUCTION: We conducted a systematic review and meta-analysis of observational studies in order to explore the relationship between erythropoietin (EPO) and hemoglobin in elderly individuals with anemia of unknown etiology (AUE) and other forms of anemia. METHODS: We searched Medline, EMBASE, Web of Science, Biosis Previews, Dissertations, and Theses in addition to meeting abstracts of the European Hematology Association and American Society of Hematology for relevant studies. The meta-analysis was conducted using pooled ratio of means (ROM) through the generic inverse variance method. RESULTS: Six studies were included in the meta-analysis, which confirmed that EPO levels were significantly lower in AUE as compared to iron deficiency anemia (ROM 0.7210; random 95% CI 0.7052 to 0.7372; P-value < 0.00001) and anemia of chronic disease (ROM 0.8995; random 95% CI 0.8362 to 0.9677; P = 0.004). EPO levels in AUE were slightly higher than levels in anemia of chronic kidney disease (ROM 1.0940; random 95% CI 1.0557, 1.1337; P < 0.00001). The heterogeneity (I2) of all analyses was 100%. CONCLUSION: Our findings suggest that erythropoietin levels in AUE, although elevated, remain inappropriately low, particularly when compared with other forms of anemia. This suggests a relative erythropoietin deficiency or a blunted erythroid cell response.

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 categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.789
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0230.003
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.070
GPT teacher head0.381
Teacher spread0.311 · 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 designSystematic review
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

Citations13
Published2016
Admission routes1
Has abstractyes

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