Ivosidenib in acute myeloid leukemia
Bibliographic record
Abstract
INTRODUCTION: Traditional treatment strategies for acute myeloid leukemia (AML) have primarily relied on standard chemotherapy regimens for four decades. Indeed, the landscape of AML therapy has evolved substantially in recent years, mainly due to the introduction of hypomethylating agents and small molecules.Bcl2 inhibitor venetoclax, Fms-like tyrosine kinase 3 (FLT3) inhibitors such as midostaurin and gilteritinib, and isocitrate dehydrogenases 1 and 2 (IDH1 and IDH2) inhibitors ivosidenib and enasidenib, as well as hedgehog (HH) pathway inhibitor glasdegib represented a significant step forward in AML therapeutic armamentarium. Smoothened (SMO) inhibitor in combination with low-dose cytarabine marks a recent milestone. AREAS COVERED: differentiation of primary mIDH1 AML blasts. Clinical data highlighted its exceptional safety profile, as a standalone therapy and in combination strategy. Additionally, comprehensive studies consistently demonstrated its effectiveness, both in monotherapy and in association with chemotherapy. EXPERT OPINION: The identified ivosidenib's strengths, including its remarkable safety record and ability to yield positive therapeutic outcomes, position it as an ideal partner for both classic chemotherapy and biological treatments, i.e. hypometilant agents and/or venetoclax. Further studies are warranted to explore strategies for overcoming the occurrence of ivosidenib resistance.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.006 | 0.003 |
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".