Bone Health in Patients With Hematopoietic Disorders of Bone Marrow Origin: Systematic Review and Meta- Analysis
Bibliographic record
Abstract
Blood cell production and bone homeostasis are physically interlinked systems that exhibit active cross-talk. We examined how bone health is affected in patients with hematopoietic disorders due to abnormal proliferation of bone marrow cells. The electronic databases Medline, Embase, PubMed, BIOSIS Previews, Web of Science, and Cochrane were searched for studies presenting numerical values for trabecular bone volume or bone mineral density in control and patients with hematopoietic disorders. We identified 5 studies for beta-thalassemia, 6 for sickle cell anemia, 2 for polycythemia vera and essential thrombocythemia, 3 for chronic myelogenous leukemia, 6 for myelofibrosis, 5 for multiple myeloma, and 4 studies each for systemic mastocytosis, lymphocytic leukemia, and hemochromatosis. The effect of the disease state on bone density was significant and negative for beta-thalassemia (r = -2.00; 95% confidence interval [CI] -3.41, -0.58; p < 0.005), sickle cell anemia (-0.91; -1.36, -0.47; p < 0.00005), chronic myelogenous leukemia (-0.55; -0.88, -0.22; p < 0005), mastocytosis (-0.99; -1.16, -0.82; p < 0.00001), lymphoblastic leukemia (-0.69; -0.98, -0.40; p < 0.00001), multiple myeloma (-0.67; -0.99, -0.35; p < 0.00005), and hemochromatosis (-1.15; -1.64, -0.66; p < 0.00001). The changes were negative but not significant for polycythemia vera (-0.16; -0.38, 0.05; p = 0.069) and essential thrombocythemia (-0.33; -0.92, 0.26; p = 0.14). In myelofibrosis, disease state was associated with increased bone density (0.74; 0.12, 1.36; p < 0.05). Bone density change significantly and negatively correlated with the level of ferritin and bone marrow cellularity but not with hemoglobin or erythropoietin. Thus, independent of hematopoietic lineage, abnormal proliferation of bone marrow cells appears to be associated with bone loss. Iron metabolism may independently contribute to bone homeostasis. © 2016 American Society for Bone and Mineral Research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".