Elective Discontinuation of Larotrectinib in Pediatric Patients With TRK Fusion Sarcomas and Related Mesenchymal Tumors
Bibliographic record
Abstract
Larotrectinib is a highly selective tropomyosin receptor kinase (TRK) inhibitor with efficacy in children with TRK fusion tumors. We evaluated patient outcomes after elective discontinuation of larotrectinib in the absence of disease progression in a protocol-defined wait-and-see subset analysis of eligible patients where treatment resumption with larotrectinib was allowed if disease progressed. We also assessed the safety and efficacy of larotrectinib in all pediatric patients with sarcoma. This cohort included 91 patients (younger than 18 years) from two clinical trials: infantile fibrosarcoma (49), other soft tissue sarcomas or related mesenchymal tumors (41), and bone sarcoma (1). Treatment-related adverse events were of maximum grade 1 or 2 in 25% and 25% of patients, respectively. The overall response rate was 87% (95% CI, 78 to 93). In the wait-and-see analysis, 47 patients discontinued larotrectinib. Median time from discontinuation to disease progression was not reached. Sixteen patients had tumor progression during the wait-and-see period. All 16 patients resumed larotrectinib, and 15 (94%) achieved disease control, with 11 objective responses. Larotrectinib continues to demonstrate durable responses with favorable safety in children with TRK fusion sarcomas. Treatment discontinuation is feasible in select patients with objective response and clinical benefit noted in those who have disease progression after elective treatment discontinuation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.000 | 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 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".