Treatment-Related Myelodysplasia and Acute Leukemia in Non-Hodgkin’s Lymphoma Patients
Bibliographic record
Abstract
PURPOSE: Standard therapies for non-Hodgkin's lymphoma (NHL) are associated with an increased risk of developing treatment-related myelodysplastic syndrome or acute myelogenous leukemia (tMDS/AML). However, there is considerable debate over the incidence or risk of tMDS/AML in NHL patients treated with any particular modality and the factors that contribute to malignant transformation. DESIGN: Conclusions were based on thorough analysis of data reported in the peer-reviewed literature and careful examination of the statistical methodology and methods for identifying cases of tMDS/AML. Unless noted, data are reported only for NHL patients, excluding Hodgkin's disease patients. RESULTS: Despite differences in methods used to identify cases and to estimate the cumulative incidence over time (actuarial v cumulative calculations), up to 10% of NHL patients treated with either conventional-dose chemotherapy or high-dose therapy and autologous stem-cell transplantation may develop tMDS/AML within 10 years of primary therapy. Kaplan-Meier estimates of the actuarial incidence, which are based on censoring of patients who died without developing tMDS/AML, can lead to artificially high estimates with large confidence intervals at later time points. Although there is much debate about the cause(s) of tMDS/AML, there is compelling evidence that alkylating agents, certain other leukemogenic agents, and total-body irradiation (TBI) cause chromosomal damage that can lead to tMDS/AML. CONCLUSION: Limiting exposure to alkylating agents and eliminating TBI from transplantation conditioning regimens may reduce the relative risk of tMDS/AML.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".