Unexplained anemia of aging: Etiology, health consequences, and diagnostic criteria
Bibliographic record
Abstract
BACKGROUND: Up to 15% of people aged 60 and over are anemic, and the prevalence of anemia increases with age. In older men and women, anemia is associated with increases in the risk of death and all-cause hospitalization, poor functional capacity, quality of life, and depression. METHODS AND RESULTS: We reviewed the literature describing anemia in aging populations, focusing on the specific diagnostic criteria of anemia and potential causes in older men and women. Even after extensive etiologic workup that involves careful medical history, physical examination, laboratory measurements, and additional studies such as bone marrow biopsy, anemia of aging is unexplained in up to 40% of older patients with anemia. As a result, treatment options remain limited. CONCLUSIONS: The prevalence of unexplained anemia of aging (UAA; also called unexplained anemia of the elderly, UAE), its deleterious impacts on health, physical function, and quality of life, and the lack of effective treatment or therapy guidelines represent a compelling unmet clinical need. In this review and consensus document, we discuss the scope of the problem, possible causes of UAA, diagnostic criteria, and potential treatment options. Because even mild anemia is strongly linked to poor clinical outcomes, it should receive clinical attention rather than simply being considered a normal part of aging.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".