Results from a phase III trial (GRID) evaluating regorafenib (REG) in metastatic gastrointestinal stromal tumour (GIST): Subgroup analysis of outcomes based on pretreatment characteristics.
Bibliographic record
Abstract
10551 Background: REG, an oral receptor kinase inhibitor with activity against KIT, PDGFR, VEGFR, FGFRs, and other oncologic targets, demonstrated significant improvement in progression-free survival (PFS) over placebo (PL) in a phase III study (GRID) of patients (pts) with advanced GIST following failure of at least imatinib (IM) and sunitinib (SU). To understand the impact of pts’ baseline characteristics on outcome, we performed an exploratory analysis of REG effects across pt subgroups based on sex, age, and mitotic index of primary GIST tissue, as well as duration and number of lines of previous therapies. Methods: Adult pts with metastatic GIST (n=199) progressing after at least IM and SU were randomized 2:1 to receive oral REG 160 mg or PL once daily for the first 3 weeks of each 4-week cycle. Subgroup analysis of centrally assessed PFS was performed, based on sex, age (<65, ≥65 years), mitotic index of primary GIST (<5, ≥5 to <10, ≥10 mits/50 hpf), duration of IM and SU treatment (<6, ≥6 to <18, ≥18 months), and number of prior anticancer treatment lines (2 or ≥3). Mitotic index data on primaries were available for 119 patients. Results: REG demonstrated improvement in PFS vs PL in all subgroups, as summarized in the Table. Conclusions: REG had substantial efficacy in all patient subgroups included in this analysis. Clinical trial information: NCT01271712. [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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".