Efficacy and safety of RET-kinase inhibitors in RET-altered thyroid cancers: a systematic review and single-arm meta-analysis
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
The RET proto-oncogene, which encodes a receptor tyrosine kinase, is an important factor in the pathogenesis of medullary and papillary thyroid cancers. Selpercatinib and pralsetinib, both specific RET-kinase inhibitors, are the only FDA-approved drugs for treating RET-altered thyroid cancer. We wanted to evaluate the safety and efficacy of selpercatinib and pralsetinib in RET-altered thyroid cancers. We searched the PubMed, Embase, Cochrane, and Clinicaltrials.gov databases for randomized controlled trials and observational studies published up to March 30, 2024, and included those that reported any of the desired endpoints. The primary endpoints were 1-year progression-free survival (PFS), objective response rate (ORR), and disease control rate (DCR). Quantitative analyses were performed using the R programming language. We included four studies with 560 patients, 510 with RET-mutant and 50 with RET-fusion thyroid cancer. The 1-year PFS was 84% (95% CI, 79-88, I 2 = 43%), ORR was 69% (95% CI, 65-73, I 2 = 0) and DCR was 93% (95% CI, 89-96, I 2 = 44%). Some important grade ≥3 adverse events were hypertension (16%; 95% CI, 11-22; I 2 = 43%), diarrhea (3%; 95% CI, 2-5; I 2 = 0), increased ALT (11%; 95% CI, 8-14; I 2 = 0) and increased AST (6%; 95% CI, 4-10; I 2 = 0). In conclusion, these findings suggest that selpercatinib and pralsetinib are efficacious and safe for use in patients with RET-altered thyroid cancer.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| 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 it