An Evaluation of Treatment Patterns and Outcomes in Elderly Patients Newly Diagnosed With Acute Myeloid Leukemia: A Retrospective Analysis of Electronic Medical Records From US Community Oncology Practices
Bibliographic record
Abstract
BACKGROUND: Many elderly patients with acute myeloid leukemia (AML) are considered ineligible for standard intensive induction therapy due to performance status and comorbidities. We analyzed treatment patterns and outcomes among elderly patients newly diagnosed with AML in the US community oncology setting. METHODS: A retrospective observational study was conducted using patient-level data from a network of US community oncology practices provided by Altos Solutions. Patients aged ≥ 60 years, diagnosed with AML between November 2005 and February 2014, with ≥ 1 recorded visit and ≥ 6 months between diagnosis and data cutoff, were included. Only patients who received active treatment or best supportive care (BSC) per National Comprehensive Cancer Network (NCCN) AML Guidelines were analyzed. RESULTS: Of 1139 patients meeting the inclusion criteria, 922 (median age 76 years) received NCCN-recommended treatments: standard induction (n = 5), low-intensity therapy (n = 425), BSC with hydroxyurea (HU) (n = 36), or BSC without HU (n = 455). For the low-intensity therapy cohort, median time from diagnosis to treatment initiation was 17 days; median duration of therapy was 5.1 months. Median overall survival (OS) from diagnosis in the low-intensity, BSC with HU, and BSC without HU groups was 12.3, 7.0, and 49.4 months, respectively. Median time to next therapy/death was 10.1 months in patients receiving low-intensity therapy. A higher proportion of patients receiving low-intensity therapy required transfusion or other supportive care versus those receiving BSC. CONCLUSIONS: As expected, OS in patients receiving low-intensity therapy or BSC with HU is poor for elderly patients with AML. Remarkably, intensive induction strategies are rarely used for older patients in community oncology practice.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".