Advances in IDH-mutant glioma management: IDH inhibitors, clinical implications of INDIGO trial, and future perspectives
Bibliographic record
Abstract
The discovery of isocitrate dehydrogenase (IDH) mutation in gliomas marked the new era of molecular classification of CNS tumors. Understanding the complex role of IDH mutation in oncogenesis led to the evaluation of novel small molecules targeting this enzyme as a potential therapeutic intervention. Vorasidenib, a brain-penetrant inhibitor of both IDH1 and IDH2-mutant enzymes, was one such agent. The phase 3 INDIGO trial evaluated vorasidenib and demonstrated its efficacy in IDH-mutant low-grade gliomas (LGG). This study established vorasidenib as an effective inhibitor of both IDH1 and IDH2-mutant enzymes, highlighting its great potential in advancing the therapeutic armamentarium for patients with LGG. While vorasidenib has been recently included in several treatment guidelines for CNS tumors, further research on the use of this novel agent, as monotherapy or in combination with other drugs, becomes imperative to exploit fully its potential in the management of IDH-mutant gliomas.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".