When Blood Disorders Meet Cancer: Uncovering the Oncogenic Landscape of Sickle Cell Disease
Bibliographic record
Abstract
Sickle cell disease (SCD) is a hemoglobinopathy characterized by hemolysis, vaso-occlusion, and systemic inflammation. Epidemiological studies identified an increased risk of leukemia, especially acute myeloid leukemia (AML), in individuals with SCD, whereas data regarding other tumors are conflicting. SCD-associated AMLs frequently display high-risk features with unfavorable karyotypes and a dismal prognosis. SCD is associated with multiple phenomena linked to carcinogenesis in other contexts, including chronic inflammation, oxidative stress, ineffective erythropoiesis, accelerated hematopoietic aging, impaired tumor immunosurveillance, and increased clonal hematopoiesis. The role and respective contribution of these disease-intrinsic mechanisms in SCD remain to be studied. Although therapies used in SCD could theoretically modulate the risk of malignancies, no data exist to support an increased or reduced risk associated with their use. The most notable exception is hematopoietic stem cell transplantation and, to a lesser extent, gene therapy, for which the conditioning and/or procedure itself is known to increase the risk of leukemia. In sum, the effect of SCD on carcinogenesis is an emerging area of investigation with data supporting specificities in SCD-associated AML. Future research is required to determine the role of treatments to mitigate the increased risk and improve the outcome of SCD-associated AML.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".