A Systematic Review and Network Meta-Analysis of Post-Imatinib Therapy in Advanced Gastrointestinal Stromal Tumour
Bibliographic record
Abstract
Background: The standard first-line systemic therapy for advanced gastrointestinal stromal tumour (GIST) is imatinib. However, most GISTS develop imatinib resistance, highlighting the need for new agents in the imatinib-refractory setting. Currently, no randomized studies have directly compared the available post–first-line treatments. Methods: In a systematic review, the MEDLINE, EMBASE, and CENTRAL databases, and American Society of Clinical Oncology abstracts to July 2014 were searched to identify randomized controlled trials that included GIST patients treated with post–first-line therapies. Hazard ratios (HRS) for progression-free (PFS) and overall survival (OS) were extracted. Direct pairwise meta-analyses and indirect comparisons using the Butcher method were performed. Results: Four studies were identified for the systematic review. One study showed that sunitinib in the second-line setting (vs. placebo) was associated with improved PFS, but not improved OS. Three studies examined the third-line setting (imatinib resumption vs. placebo, regorafenib vs. placebo, nilotinib vs. best supportive care). In the third-line settings, the two placebo-controlled and the non-placebo-controlled trials showed significant heterogeneity (I2 = 98%). Indirect comparisons of imatinib resumption and regorafenib suggested that the hr for PFS was 0.59 (95% confidence interval: 0.31 to 1.12; p = 0.10), trending in favour of regorafenib. Indirect comparisons found that toxicities were higher in the regorafenib group, with a risk difference of 27.8% for any-grade toxicities and 19.5% for grades 3 and 4 toxicities. Conclusions: Because a head-to-head study of imatinib resumption compared with regorafenib is unlikely ever to be conducted, our study suggests that, in terms of PFS, regorafenib might be the preferred treatment. However, given the increased toxicity observed with regorafenib, clinicians should interpret that evidence with caution at an individual patient level.
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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 | 0.022 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.042 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".