Bibliographic record
Abstract
Abstract The NTRK1, NTRK2, and NTRK3 genes encode the TRKA, TRKB, and TRKC receptors, critical for nervous system development. Gene fusions involving neurotrophic tyrosine receptor kinase (NTRK) are found in various cancers. In the pediatric population, NTRK gene fusions have been identified in up to 5.3% of high-grade gliomas (HGGs) and 2.5% of low-grade gliomas (LGGs). The prevalence is notably higher in young children, particularly in infantile hemispheric gliomas, where the fusion frequency is about 20%. Targeted therapies with TRK inhibitors (TRKi), including larotrectinib and entrectinib, have shown promising efficacy with rapid and durable responses for patients with LGGs and HGGs. TRKi are usually well tolerated, but on-target and off-target adverse events have been reported, such as increased AST/ALT, fatigue, decreased neutrophil, weight gain, and fractures with entrectinib. Resistance to TRKi arises from on-target mutations or new pathway activations, with second-generation inhibitors addressing some resistant cases. Despite efficacy, challenges remain in diagnosis, treatment access, and long-term safety, particularly regarding cognitive development and bone health. Overall, TRKi represent a significant advance for treating NTRK fusion-positive CNS tumors, especially in pediatric populations, offering new hope for patients with limited treatment options. Further studies are required to optimize their use and address unresolved challenges.
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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".