CHIP ahoy: charting a decade of discovery in clonal hematopoiesis
Bibliographic record
Abstract
Clonal hematopoiesis (CH) involves the expansion of hematopoietic stem cells with age-acquired mutations linked to myeloid malignancy. Advances in next-generation and single-cell sequencing, along with computational modeling, have expanded our ability to detect both common and rare CH drivers, including single-nucleotide variants and mosaic chromosomal alterations, with increasing sensitivity. While sequencing methods differ in accuracy, cost, and ability to detect low-frequency variants, they have deepened our understanding of CH biology. A growing body of evidence has identified both somatic drivers, such as variants in DNMT3A, TET2, and ASXL1, and germline genetic variants that modify CH risk, highlighting the complex interplay between acquired and inherited factors. These collective discoveries are guiding the development of targeted therapies and interventions, particularly for individuals at risk of progression to myeloid neoplasms or cardiovascular disease. Additionally, CH is emerging as a clinically relevant factor in the treatment of solid tumors, in which it may influence the tumor microenvironment, response to treatment and the risk of therapy-related complications. Risk stratification models are facilitating earlier identification and monitoring of high-risk individuals, enabling personalized treatment decisions. The scope of CH management continues to expand, from surveillance to intervention, with ongoing trials testing preventive strategies in high-risk populations. Emerging trial frameworks emphasize risk stratification, age-appropriateness, inclusive recruitment, decentralized trial models, and the use of traditional clinical and novel endpoints. Together, these advances reflect a shift from passive observation to proactive intervention, charting a course for early detection, precision treatment, and prevention in CH care.
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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 | 0.022 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".