A globally applicable “triple <scp>A</scp>” risk model for essential thrombocythemia based on <scp>A</scp>ge, <scp>A</scp>bsolute neutrophil count, and <scp>A</scp>bsolute lymphocyte count
Bibliographic record
Abstract
Abstract We examined the individual prognostic contribution of absolute neutrophil (ANC), lymphocyte (ALC), and monocyte (AMC) counts, on overall (OS), leukemia‐free (LFS), and myelofibrosis‐free (MFFS) survival in essential thrombocythemia (ET). Informative cases ( N = 598; median age 59 years; females 62%) were retrospectively accrued from a Mayo Clinic database: JAK2 59%, CALR 27%, triple‐negative 11%, and MPL 3%; international prognostic scoring system for ET (IPSET) risk high 21%, intermediate 42%, and low 37%; 7% (37/515) had abnormal karyotype and 10% (21/205) adverse mutations ( SF3B1/SRSF2/U2AF1/TP53 ). At median 8.4 years, 163 (27%) deaths, 71 (12%) fibrotic, and 20 (3%) leukemic transformations were recorded. Multivariable analysis resulted in HR (95% CI) of 16.5 (9.9–27.4) for age > 70 years, 3.7 (2.3–6.0) for age 50–70 years, 2.4 (1.7–3.3) for ANC ≥8 × 10 9 /L, and 1.9 (1.4–2.6) for ALC <1.7 × 10 9 /L. The corresponding HR‐based scores were 4, 2, 1, and 1, resulting in an new 4‐tiered AgeAncAlc (AAA; triple A) risk model: high (5–6 points; median survival 8 years; HR 30.1, 95% CI 17.6–54), intermediate‐2 (4 points; median 13.5 years; HR 12.7, 95% CI 7.1–23.0), intermediate‐1 (2–3 points; median 20.7 years; HR 3.8, 95% CI 2.3–6.4) and low (0–1 points; median 47 years). The AAA model (Akaike Information Criterion [AIC] 621) performed better than IPSET (AIC 647) and was subsequently validated by an external University of Florence ET cohort ( N = 485). None of the AAA variables predicted LFS while ALC <1.7 × 10 9 /L was associated with inferior MFFS ( p = .01). Adverse mutations ( p < .01) and karyotype ( p < .01) displayed additional prognostic value without disqualifying the prognostic integrity of the AAA model. This study proposes a simple and globally applicable survival model for ET, which can be used as a platform for further molecular refinement. This study also suggests a potential role for immune‐related biomarkers, as a prognostic tool in myeloproliferative neoplasms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".