Risk prediction for clonal cytopenia: multicenter real-world evidence
Bibliographic record
Abstract
ABSTRACT: Clonal cytopenia of undetermined significance (CCUS) represents a distinct disease entity characterized by myeloid-related somatic mutations with a variant allele fraction of ≥2% in individuals with unexplained cytopenia(s) but without a myeloid neoplasm (MN). Notably, CCUS carries a risk of progressing to MN, particularly in cases featuring high-risk mutations. Understanding CCUS requires dedicated studies to elucidate its risk factors and natural history. Our analysis of 357 patients with CCUS investigated the interplay between clonality, cytopenia, and prognosis. Multivariate analysis identified 3 key adverse prognostic factors: the presence of splicing mutation(s) (score = 2 points), platelet count of <100 × 109/L (score = 2.5), and ≥2 mutations (score = 3). Variable scores were based on the coefficients from the Cox proportional hazards model. This led to the development of the clonal cytopenia risk score (CCRS), which stratified patients into low- (score of <2.5 points), intermediate- (score of 2.5 to <5), and high-risk (score of ≥5) groups. The CCRS effectively predicted 2-year cumulative incidence of MN for low- (6.4%), intermediate- (14.1%), and high-risk (37.2%) groups, respectively, by the Gray test (P < .0001). We further validated the CCRS by applying it to an independent CCUS cohort of 104 patients, demonstrating a c-index of 0.64 (P = .005) in stratifying the cumulative incidence of MN. Our study underscores the importance of integrating clinical and molecular data to assess the risk of CCUS progression, making the CCRS a valuable tool that is practical and easily calculable. These findings are clinically relevant, shaping the management strategies for CCUS and informing future clinical trial designs.
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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 | 0.020 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".