Surgery plus TKIs therapy for gastrointestinal stromal tumors
Bibliographic record
Abstract
Abstract Introduction In this era of tyrosine kinase inhibitors (TKIs), the clinical benefit of surgery for patients with metastatic or recurrent gastrointestinal mesenchymal tumor (GIST) is not well defined. The aim of our study was to demonstrate the survival advantage of adding surgery in patients with recurrent or metastatic GIST. Methods A systematic search of PubMed, Web of Knowledge, Ovid’s database was conducted. Relevant studies published by 31 July 2022 on the role of surgery in recurrent or metastatic GIST were identified. Research quality was assessed using the Newcastle-Ottawa Quality Assessment Scale. Results Eight studies involving 842 patients were included. The four included studies covered 3-year survival and included 441 patients, of whom 302 received TKIs, and 139 received TKIs plus surgery. 3-year overall survival was significantly higher in the TKIs plus surgery group than in the TKIs group (OR=2.37, 95% CI 1.45–3.88, P = 0.001). The 5-year overall survival was 69.0% in the TKIs plus surgery group compared with 49.1% in the TKIs only group. Survival was significantly higher in TKIs plus surgery group (OR = 2.69, 95%Cl 1.49–4.86, P=0.001). Four studies, including 453 patients, indicated 3-year progression-free survival (PFS). The pooled analysis revealed the TKIs plus surgery group did have a better PFS than the TKIs only group (OR = 4.02, 95% CI: 1.45–11.16, P=0.008). Three included studies focused on gastrointestinal stromal tumor liver metastasis (GLM). The role of surgery plus TKIs had statistically significant better 5-year overall survival as compared with TKI treatment alone (OR = 2.34, 95% Cl 1.30–4.22, P=0.005). Conclusions Treatment with surgical resection and TKIs could significantly improve the prognosis of patients with recurrent or metastatic GIST.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".