Neoadjuvant Treatment With Regorafenib and Capecitabine Combined With Radiotherapy in Locally Advanced Rectal Cancer: A Multicenter Phase Ib Trial (RECAP)–SAKK 41/16
Bibliographic record
Abstract
BACKGROUND: The multi tyrosine kinase inhibitor regorafenib is active in metastatic colorectal cancer. Improvement in clinical outcome by adding regorafenib to long-course chemoradiotherapy (LcCRT) was investigated in molecularly undefined LARC. METHODS: d1-d38 and 28 fractions of 1.8Gy (50.4Gy). Regorafenib was added d1-14 and d22-35 in 3 dose escalation (DE) cohorts (40mg/80mg/120mg). The recommended dose (RD) was used for the expansion (EXP) cohort. Primary endpoints were dose-limiting toxicity (DLT) for DE and pathological response (near-complete regression [npCR] or complete regression [pCR]) for EXP. RESULTS: Overall, 25 patients were included. Two DLTs occurred at the regorafenib dose level of 120 mg, thereby establishing the RD at 80mg daily. Among the 19 patients who were treated at the RD, 8 (42.1%; 1-sided 80% confidence interval [CI] (lower bound): 30.7%; 95% CI, 20.3%-66.5%) reached the primary endpoint (5 [26.3%] had npCR and 3 [15.8%] pCR). One additional patient received no surgery due to clinical complete response. All patients had R0 resections and clear circumferential margins. Postoperative complications occurred in 6 patients (35.3%). The most common grade ≥ 3 treatment-related adverse event in the EXP cohort was diarrhea (2 patients). CONCLUSION: Adding regorafenib 80 mg to LcCRT in LARC resulted in both primary endpoints being met and yielded an expected pathological response rate. Toxicity was manageable, and postoperative complications were as expected.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".