Acute myeloid leukemia induction in the age of novel therapeutic agents
Bibliographic record
Abstract
Acute myeloid leukemia (AML) is a malignant neoplasm of the myeloid lineage characterized by the uncontrolled proliferation of immature myeloid blasts in the bone marrow and peripheral blood. AML is a heterogenous disease which occurs across the age spectrum, although with an increasing incidence with age. For decades, first-line, curative-intent therapy has been based on intensive therapy with anthracycline (typically daunorubicin or idarubicin) plus cytarabine (3+7), followed by additional consolidative chemotherapy and/or allogeneic stem cell transplantation. While improvements over the decades in overall survival have been observed, until recently this has been driven largely by advancements in supportive care leading to reduction in treatment-related mortality and allowing a greater proportion of patients (particularly older individuals) to safely undergo intensive therapy induction and consolidation. Despite this, five-year overall survival (OS) rates in older individuals are as low as 5% (age > 70). Although OS for patients age 15-39 is now in the range of 50%-60%, a large portion of patients still succumb to their disease. Cytogenetic and molecular profiling has led to defined risk categories, and complete risk stratification for all patients eligible for intensive therapy is crucial to aiding in the selection of optimal induction and post- remission therapy. In recent years, an improved understanding of AML biology and genetics has led to the approval of a number of novel therapies for patients deemed fit and unfit for intensive therapy, which may finally be moving the needle beyond 3+7. This article will review a current approach to AML induction patients eligible for intensive therapy, with a focus on the utilization of available novel agents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".