A population-based study of chronic neutrophilic leukemia in the United States: 2004 to 2015.
Bibliographic record
Abstract
e19538 Background: Chronic neutrophilic leukemia (CNL) is a rare and aggressive myeloproliferative neoplasm with poor survival. No standard of care exists. Case reports describe allogeneic stem cell transplantation (ASCT) as the only curative therapy. Using the National Cancer Database (NCDB), we studied prognostic factors and survival outcomes of CNL. Methods: Patients diagnosed in 2004-2015 were included. 9963/3 was the ICD-O-3 code. Overall survival (OS) was analyzed using the Kaplan-Meier method. Hazard ratios (HR) with confidence intervals (CI) were calculated using Cox proportional hazards model. Variables significant in univariate analysis were included in a multivariate analysis. Data on incidence was obtained from the Surveillance, Epidemiology, and End Results Program (SEER) 21 Registries (2004-2015). The analysis was done with SEER*Stat software. Incidence was age-adjusted to the U.S. 2000 standard population. Results: A total of 121 CNL patients were in NCDB. The median age at diagnosis was 70 years (range 21-90); 74 (61%) were males and 107 (88%) were ≥ 50 years. The median follow up time was 5.8 years (interquartile range [IQR] 2.9-7.4). 32 (26%) had a Charlson Deyo score (CDS) > 1. 80 (70%) had government insurance, while 34 (30%) had private insurance. Median OS was 2.2 years (IQR 0.8-6.7). OS was 70% at 1 year, 29% at 5 years, and 11% at 10 years. The year of diagnosis was not associated with improved OS (HR: 0.95 (95% CI: 0.88-1.02), p =0.14). 71 (59%) patients received chemotherapy. The median time to chemotherapy from the day of diagnosis was 15 days (IQR 7-35). The OS of those who received chemotherapy was 71% at 1 year and 27% at 5 years versus 69% and 32% at 1- and 5- years for those who did not ( p= 0.38). 2 patients received ASCT (age 52 and 61 years) as initial therapy and both were alive at 5 years. Factors predicting inferior OS on univariate analysis were age ≥ 50 years at diagnosis ( p= 0.005), male ( p< 0.001), CDS > 1 ( p= 0.038), and government insurance ( p= 0.004). The table shows the multivariate (MV) analysis. The overall incidence of CNL was 0.1 cases/1,000,000 individuals and did not change significantly from 2004-2015. The incidences by male, female, White, Black, and American Indian/Alaska Native were also 0.1 cases/1,000,000 individuals. Conclusions: OS and incidence of CNL have not changed over the years. Age ≥ 50 years, male gender, and use of government insurance predicted inferior OS. [Table: see text]
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".