Investigating the impact of IKZF1 SNPs rs4132601 and rs11978267 on acute lymphoblastic leukemia: a comprehensive meta-analysis
Bibliographic record
Abstract
OBJECTIVE: This meta-analysis investigates the association between acute lymphoblastic leukemia (ALL) susceptibility and IKZF1 gene SNPs. METHODS: Utilizing EMBASE, PubMed, and other databases, the study evaluated methodological quality through the Newcastle-Ottawa Scale (NOS) scoring and Hardy-Weinberg Equilibrium (HWE) value. The present meta-analysis used Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines. Review Manager 5.4 software was employed for data analysis, emphasizing genetic variants' significance (p < 0.05). Visualizations were achieved using funnel and Circos plots. RESULTS: A significant association was found between rs4132601 and ALL across genetic models, contrasting with the non-significant correlation for rs11978267. The findings underscore the complex interplay of genetic factors in ALL susceptibility, particularly related to IKZF1 SNPs. Ethnicity emphasizes the importance of diverse population considerations. CONCLUSION: This meta-analysis highlights the significance of rs4132601 in ALL's genetic foundation, suggesting potential advancements in diagnostics. The lack of correlation for rs11978267 highlights the complexity of its genetic association. Future studies should prioritize larger, diverse samples for a comprehensive understanding and improved strategies for ALL diagnoses and treatments.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.031 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".