Survival in Elderly Patients Diagnosed With Acute Myeloid Leukemia: A Hospital-Based Study
Bibliographic record
Abstract
Background: Acute myeloid leukemia (AML) is a hematological neoplasm that is more frequent in elderly patients. The objective of this study was to evaluate elderly patients' survival with de novo AML and acute myeloid leukemia myelodysplasia-related (AML-MR), treated with intensive and less-intensive chemotherapy and supportive care. Methods: A retrospective cohort study was conducted in Fundacion Valle del Lili (Cali, Colombia), between 2013 and 2019. We included patients ≥ 60 years old diagnosed with AML. The statistical analysis considered the leukemia type ( de novo vs. myelodysplasia-related) and treatment (intensive chemotherapy regimen, less-intensive chemotherapy regimen, and without chemotherapy). Survival analysis was performed using Kaplan-Meier method and Cox regression models. Results: A total of 53 patients were included (31 de novo and 22 AML-MR). Intensive chemotherapy regimens were more frequent in patients with de novo leukemia (54.8%), and 77.3% of patients with AML-MR received less-intensive regimens. Survival was higher in the chemotherapy group (P = 0.006), but with no difference between chemotherapy modalities. Additionally, patients without chemotherapy were 10 times more likely to die than those who received any regimen, independent of age, sex, Eastern Cooperative Oncology performance status, and Charlson comorbidity index (adjusted hazard ratio (HR) = 11.6, 95% confidence interval (CI) 3.47 - 38.8). Conclusions: Elderly patients with AML had longer survival time when receiving chemotherapy, regardless of the type of regimen. J Hematol.2023;12(1):7-15 doi: https://doi.org/10.14740/jh1055
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".