Bibliographic record
Abstract
After taking all of the CME courses in this supplement the reader will be able to: Describe the basic biology of various leukemias, multiple myeloma, and myelodysplastic syndrome (MDS). Discuss new targeted treatment strategies for hematologic malignancies. Understand the rationale for the use of nontraditional cytotoxic agents such as arsenic trioxide in the treatment of hematologic malignancies. Examine the role of arsenic trioxide and other novel agents in early-versus accelerated-stage hematologic disease. Discuss the preclinical and clinical efficacy of arsenic trioxide and various agents in treating acute promyelocytic leukemia, MDS, and multiple myeloma. Access CME test online and receive one hour category 1 credit at CME.TheOncologist.com Arsenic trioxide (As2O3; ATO) has considerable efficacy in the treatment of relapsed acute promyelocytic leukemia (APL), inducing partial differentiation and promoting apoptosis of malignant promyelocytes. Although initial studies focused on the role of the characteristic APL fusion protein, PML-RARα, in mediating response to ATO, subsequent investigations have revealed that ATO acts on numerous intracellular targets. ATO broadly affects signal transduction pathways and causes a wide range of alterations leading to apoptosis. Key mediators of sensitivity to ATO-induced apoptosis include intracellular glutathione and hydrogen peroxide (H2O2). The loss of inner mitochondrial membrane potential is also an important step in ATO-mediated cell killing. Cellular and physiologic pathways affected by ATO provide some clues as to the mechanisms for the biologic effects of ATO. Recent research has shown that hematologic cancers other than APL and solid tumors derived from several tissue types may be responsive to monotherapy or combination therapy with ATO. A better understanding of the mechanisms of action of ATO may help guide the use of ATO for the treatment of a wide variety of malignancies and allow its potential in cancer therapy to be fully realized.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".