Treating the Elderly Patient with Acute Myelogenous Leukemia
Bibliographic record
Abstract
Among patients with acute myeloid leukemia (AML), treatment regimens and outcomes may differ among younger and older adults. Although there is no clear dividing line when considering age in AML, in most studies, "older adults" was defined as over age 60.The management of older patients with AML is a difficult challenge (1). Older adults are more likely to have comorbidities that can limit treatment options; disease tends to be more aggressive biologically; and outcomes are worse than in younger patients. Decisions regarding the optimal treatment of acute myelogenous leukemia in the elderly patient requires the consideration of multiple factors. Population-based studies have demonstrated that, for all age groups, aggressive therapy results in improved survival and quality of life when compared with palliative care. The optimal induction and post remission regimen for older patients has yet to be determined. Furthermore, not all patients are candidates for such therapy. Consideration of patient and disease-related factors can help to determine the appropriateness of intensive therapy in a given patient. For those patients for whom aggressive induction therapy does not seem to be in their best interest, novel agents are being investigated that will hopefully address the issues of induction death and early relapse associated with these patient populations. This topic review will discuss the treatment of older adults with 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".