The Role of Surgical Resection Following Tyrosine Kinase Inhibitors Treatment in Patients with Advanced Gastrointestinal Stromal Tumors: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
The benefit of surgical resection for advanced gastrointestinal stromal tumors (GISTs) following tyrosine kinase inhibitors (TKIs) treatment was still under debate. The present meta-analysis was designed to assess the value of surgical resection for the prognosis of patients with metastatic, recurrence and unresectable GISTs. Methods A systematic search of PubMed Central, PubMed, EMBASE and the Cochrane Library database was performed. Relevant studies of the role of surgery in advanced GISTs published before 1 May 2019 were identified. The quality of studies was assessed by the Newcastle-Ottawa Quality Assessment Scale. The progression-free survival (PFS) and overall survival (OS) were assessed through software Stata 15.0. Results A total of 6 retrospective studies including 655 patients were analyzed. The pooled result revealed that surgical resection group was associated with better PFS (HR = 2.08; 95% CI: 1.58 to 2.76; P0.001) and better OS (HR = 2.13; 95% CI: 1.59 to 2.85; P0.001) compared with TKIs treatment alone group. Conclusions Surgical resection following TKIs treatment could significantly improve the prognosis of patients with advanced GISTs.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.024 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".