Sex and Gender in Myeloid and Lymphoblastic Leukemias and Multiple Myeloma: From Molecular Mechanisms to Clinical Outcomes
Bibliographic record
Abstract
Biological sex and gender factors significantly influence the pathogenesis, progression, and treatment response in hematologic malignancies. This comprehensive review examines sex-specific differences in acute myeloid leukemia, acute lymphoblastic leukemia, chronic myeloid leukemia, and multiple myeloma through systematic analysis of the peer-reviewed literature published between 2014-2024 and identified through structured searches of PubMed, Web of Science, and MEDLINE databases. Epidemiological data demonstrate higher disease incidence (57% male vs. 43% female in MM, 63% male vs. 37% female in AML hospitalizations for ages 18-39) and inferior outcomes in male patients across malignancy types (5-year relative survival rates of 48.8% vs. 60.4% in females with AML), while female patients exhibit superior survival despite experiencing greater treatment-related toxicities. Our analysis reveals consistent sex-specific patterns in molecular mechanisms, including distinct mutational profiles, differences in immune system function, and sex-based pharmacokinetic variations that collectively suggest the necessity for sex-differentiated treatment approaches. The review identifies reproducible patterns across diseases, particularly in cytogenetic and molecular characteristics, with females demonstrating favorable prognostic mutations in leukemias and higher rates of chromosomal abnormalities in multiple myeloma. Despite these identifiable patterns, significant knowledge gaps persist regarding the underlying mechanisms of sex-based outcome differences. Incorporating sex and gender considerations into precision medicine frameworks represents a critical advancement toward optimizing treatment strategies and improving clinical outcomes for patients with hematologic malignancies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".