LGG-01. Managing side effects of MAPK inhibitor therapies in pediatric populations: a Delphi consensus initiative
Bibliographic record
Abstract
Abstract Pediatric low-grade gliomas (pLGGs), among other pediatric brain/CNS tumors, are often driven by activating mutations in the MAPK pathway; MAPK inhibitors (MAPKis) have emerged as key treatments. However, challenges have arisen regarding their toxicity and potential late effects, creating a need for additional guidance in diagnosing, monitoring, and managing adverse events (AEs). Here, we report preliminary findings from a literature search supporting a Delphi consensus initiative, aiming to provide recommendations for clinicians to improve management of AEs with MAPKis. PubMed, Web of Science, and Cochrane Trials databases were searched for articles in English published between 2010–2024. Congress abstracts published between 2018–2024 were also considered. Up to 100 multidisciplinary global experts will aim to reach consensus on statements conceived by an international steering committee, supported by the literature search. In total, 638 publications were identified; 208 were considered relevant for data extraction. Most publications were case reports/series (64), retrospective studies (59), or phase 1/2 trials (46). The most common cancer types reported were pLGG and plexiform neurofibroma. In total, 152 publications reported data for MEK inhibitors (most commonly trametinib and selumetinib) and 116 reported data for BRAF/RAF inhibitors (most commonly dabrafenib and vemurafenib). Publications most frequently reported AE incidence, severity, and dose modifications, and less frequently reported AE management strategies, risk factors, and patient experiences. Several classes of AE, including cutaneous, gastrointestinal, hematological, cardiac, laboratory abnormalities, and general constitutional symptoms, were reported with all MAPKi types. Cutaneous toxicities were consistently common and were the AEs for which management strategies were most frequently reported. There is a paucity of information regarding AE prophylaxis, management, risk factors, and patient experiences in pediatric patients receiving MAPKis. Our findings will form the basis of a series of consensus statements and recommendations to guide and improve management of AEs with MAPKis.
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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 | 0.169 | 0.162 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".