Efficacy and safety of larotrectinib in TRK fusion-positive primary central nervous system tumors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Larotrectinib is a first-in-class, highly selective tropomyosin receptor kinase (TRK) inhibitor approved to treat adult and pediatric patients with TRK fusion-positive cancer. The aim of this study was to evaluate the efficacy and safety of larotrectinib in patients with TRK fusion-positive primary central nervous system (CNS) tumors. METHODS: Patients with TRK fusion-positive primary CNS tumors from two clinical trials (NCT02637687, NCT02576431) were identified. The primary endpoint was investigator-assessed objective response rate (ORR). RESULTS: As of July 2020, 33 patients with TRK fusion-positive CNS tumors were identified (median age: 8.9 years; range: 1.3-79.0). The most common histologies were high-grade glioma (HGG; n = 19) and low-grade glioma (LGG; n = 8). ORR was 30% (95% confidence interval [CI]: 16-49) for all patients. The 24-week disease control rate was 73% (95% CI: 54-87). Twenty-three of 28 patients (82%) with measurable disease had tumor shrinkage. The 12-month rates for duration of response, progression-free survival, and overall survival were 75% (95% CI: 45-100), 56% (95% CI: 38-74), and 85% (95% CI: 71-99), respectively. Median time to response was 1.9 months (range 1.0-3.8 months). Duration of treatment ranged from 1.2-31.3+ months. Treatment-related adverse events were reported for 20 patients, with grade 3-4 in 3 patients. No new safety signals were identified. CONCLUSIONS: In patients with TRK fusion-positive CNS tumors, larotrectinib demonstrated rapid and durable responses, high disease control rate, and a favorable safety profile.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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 it