Erythropoietin in anemia of unknown etiology: A systematic review and meta-analysis
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.030 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".