A systematic review of post first-line treatments for advanced gastrointestinal stromal tumor (GIST): Direct pairwise meta-analyses and indirect comparisons.
Bibliographic record
Abstract
195 Background: Treatment for metatastatic or unresectable GIST is with imatinib. The majority of patients eventually acquire imatinib resistance, prompting the development of a growing number of agents as post-first-line treatment. Currently no studies directly compare these treatments. Methods: A systematic review was performed through MEDLINE, EMBASE, CENTRAL, and ASCO meeting abstracts up to July 2014 to identify randomized controlled trials that included GIST patients who were previously treated with a first-line chemotherapy for advanced disease. Progression-free survival (PFS) and overall survival (OS) with 95% credible regions were extracted using the Parmar method. Direct pairwise meta-analyses and indirect comparisons using the Bucher method were performed. Results: Four studies were identified for the systematic review. 1 study (n=312) showed that sunitinib in the second-line setting (vs. placebo) improved PFS but not OS. 3 studies (n=528) examined the third-line setting (imatinib resumption vs. placebo; regorafenib vs. placebo; nilotinib vs. best supportive care with or without imatinib or sunitinib). Of the 3 third-line studies, there was significant heterogeneity between placebo-controlled trials and the non-placebo controlled trial (I2= 98%). Direct pairwise meta-analysis using random-effects of the 2 placebo-controlled studies showed that the PFS hazard ratio (HR) was 0.63 (0.22-0.61, p=0.0001), whereas the PFS HR for the non-placebo controlled study was 0.90 (0.65-1.26, p=0.56). These 2 HRs are different statistically (interaction: p=0.002). Indirect comparisons of imatinib resumption vs. regorafenib suggested that the PFS HR was 0.59 (0.31-1.11, p=0.10), trending in favor of regorafenib. OS HRs were not significant for direct or indirect comparison in the third-line setting. Conclusions: The number and size of completed phase III studies and the lack of a standard comparator arm limit the use of direct and indirect comparison methods to determine the best therapeutic option for patients who have progressed on imatinib. At this time, clinicians should interpret the available evidence at an individual 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 | 0.028 | 0.068 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.051 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".