First-Line Treatments for Patients with Acute Myeloid Leukemia: A Literature Review
Bibliographic record
Abstract
Introduction: Acute myeloid leukemia (AML) is a type of cancer with a very low five-year survival rate (19%), which motivates research into numerous treatment options to improve survivability and remission rates. Here, some common treatments will be briefly discussed to provide a brief foray into AML treatment. As a very general statement, chemotherapy is the most common treatment for this condition. This is a general statement because different malignancies and multi-morbidities can heavily modify treatment options. These options each have their merits and critical differences, which should be discussed. Some significant medications are all-trans retinoic acid, interleukin II, lenalidomide, and colony-stimulating factors. Some targeted therapies would focus on FMS-like tyrosine kinase 3 inhibitors, isocitrate dehydrogenase 1 and 2 inhibitors, Gemtuzumab ozogamicin, B-cell leukemia/lymphoma-2 inhibitors, and hedgehog pathway inhibitors. Methods: A literature review was performed to summarize all available research on the different categories and types of therapeutic options for AML. Patients at all stages of AML were considered, including newly diagnosed patients and those with relapsed or drug-resistant disease. Results: Various treatments had their efficacy listed with information gained from various types of studies. The main "efficacy" focuses were remission rates and survivability over varied time periods (i.e., short-term versus long-term). Discussion: This literature review provided insight into the current treatments of AML and noted that a direct comparison between every treatment type is not possible. Furthermore, several therapies are undergoing clinical trials in combination with chemotherapy, making it difficult to isolate their independent effects. Conclusion: Treatment options for AML may be affected by the AML subtype, various prognostic factors, cytogenetics, and the patient's medical history. This review aids in accessibly summarizing essential information about AML and different therapeutic options including drug targets as well as identifying future areas of research.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".