Chemotherapy for patients with gastric cancer after complete resection: A network meta-analysis
Bibliographic record
Abstract
AIM: To conduct a network meta-analysis to evaluate the effectiveness of different chemotherapy regimens for patients with gastric cancer. METHODS: PubMed (1966-2011.12), the Cochrane Library (2011 Issue 2) and EMBASE (1974-2011.12) were searched with the terms "gastric cancer" and "chemotherapy", as well as the medical subject headings. References from relevant articles and conferences were also included. Patients who had previous gastric surgery, radiation before or after surgery or chemotherapy before surgery were excluded. In this study, only randomized controlled trials (RCTs) were considered, and the end-point was the overall mortality. Direct comparisons were performed using traditional meta-analysis whereas indirect comparisons were performed using network meta-analysis. RESULTS: In total, 31 RCTs with 7120 patients were included. Five chemotherapy regimens, fluorouracil (FU) + BCNU, FU + methyl-CCNU (mCCNU), FU + cisplatin, FU + anthracyclines and FU + mitomycin c (MMC) + cytarabine (Ara-c), were found to be less beneficial in terms of overall mortality. In contrast, four chemotherapy regimens were effective for the patients after surgery, including FU + MMC + adriamycin (FMA), FU + MMC (FM), Tegafur and MMC, There was no significant difference in terms of overall mortality among these regimens. The evidence for the FM regimen and MMC regimen was poor. Additionally, the FMA regimen, which includes a variety of chemotherapy drugs and causes many side effects, was not better than the Tegafur regimen. CONCLUSION: Although the four chemotherapy regimens were effective in patients with gastric cancer after surgery and the overall mortality revealed no significant difference among them in the network meta-analysis, thorough analysis of the results recommends Tegafur as the first-line adjuvant chemotherapy regimen for patients after complete resection.
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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 | 0.023 | 0.037 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.052 |
| Bibliometrics | 0.005 | 0.005 |
| 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".