Maintenance therapy with bevacizumab with or without erlotinib in metastatic colorectal cancer (mCRC) according to KRAS: Results of the GERCOR DREAM phase III trial.
Bibliographic record
Abstract
3515 Background: The primary analysis of DREAM demonstrated that a maintenance therapy (MT) with bevacizumab (Bev) + EGFR TKI erlotinib (E) significantly improved progression-free survival (PFS) after a 1st-line Bev-based induction therapy (IT) in patients (pts) with unresectable mCRC. Methods: Pts were randomized to MT after an IT with FOLFOX-bev or XELOX-bev or FOLFIRI-bev between Bev alone (Bev 7.5 mg/kg q3w; arm A) or Bev+E (Bev 7.5 mg/kg q3w, E 150 mg/d ; arm B) until PD or unacceptable toxicity. Primary endpoint was PFS on MT. Secondary endpoints included PFS from inclusion, overall survival (OS) and safety. The impact of KRAS tumor status on treatment efficacy was evaluated in an exploratory analysis. Results: 700 pts were registered and 452 pts were randomized (228 in arm A, 224 in arm B). KRAS status was available for 413/452 (91%) pts. The median duration of MT was 3.6 m. Results for MT are presented below (Table). In the registered population, median OS was 24.9m (22.5 – 27.3). Conclusions: Maintenance treatment with bev + erlotinib increases PFS over maintenance with bev alone in pts with mCRC but does not prolong OS. Further follow-up will determine the impact of 2nd or 3rd line anti-EGFR Mabs in this study. Contrasting with anti-EGFR Mabs, KRAS tumor status is not mandatory to select pts with mCRC for treatment with erlotinib. Clinical trial information: NCT00265824. [Table: see text]
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".