The changing landscape of geriatric care in acute myeloid leukemia: a 5-year analysis of inpatient mortality predictors, trends in mortality, and chemotherapy use
Bibliographic record
Abstract
Background This study examined inpatient mortality factors in geriatric patients with acute myeloid leukemia (AML) using data from the 2016 to 2020 National Inpatient Sample.Methods Identifying patients through ICD-10 codes, a total of 127,985 individuals with AML were classified into age categories as follows: 50.58% were 65 to 74 years, 37.74% were 75 to 84 years, and 11.68% were 85 years or older. Statistical analysis, conducted with STATA, involved Fisher’s exact and Student’s t tests for variable comparisons. Mortality predictors were identified through multivariate logistic regression.Results Various hospital and patient-level factors, including an increase in age, race, a higher Charlson Comorbidity Index score, insurance status, and specific comorbidities such as atrial fibrillation and protein-calorie malnutrition, independently elevated the risk of inpatient mortality. Asthma, hyperlipidemia, and inpatient chemotherapy were linked to lower mortality. Although there was no statistically significant mortality rate change from 2016 to 2020, a decline in chemotherapy use in the eldest age group was noted.Conclusion This study highlights the complexity of factors influencing inpatient mortality among geriatric patients with AML, emphasizing the need for personalized clinical approaches in this vulnerable population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".