Bevacizumab in metastatic small-bowel adenocarcinoma: A systematic review and meta-analysis
Bibliographic record
Abstract
Cancers of the small bowel could account for less than 5% of all gastrointestinal malignancies. Of these tumors, adenocarcinomas were the major histologic subtype and generally carried a poor prognosis. High expression of vascular epithelial growth factor (VEGF) could be seen in small bowel adenocarcinomas. A systematic review was conducted here to determine if bevacizumab, a recombinant humanized antibody against VEGF, could offer clinical benefit among patients with metastatic small bowel adenocarcinoma when combined with chemotherapy. A search for relevant published and unpublished studies was performed using PubMed, ScienceDirect, Google Scholar, the American Society of Clinical Oncology meetings library, ClinicalTrials.gov, and ISRCTN registry. Information on study design, methods, intervention, and outcomes were extracted from selected eligible studies. Methodological quality was then assessed using the Newcastle-Ottawa Scale. There was a significant improvement in mean overall survival with the addition of bevacizumab with chemotherapy versus chemotherapy alone. The use of bevacizumab with chemotherapy, likewise improved progression-free survival and objective response rate compared to chemotherapy alone. Continued use of bevacizumab beyond first progression also appeared to show benefit. The conduct of prospective controlled studies by consortia to offset the rarity of small bowel adenocarcinomas could further elucidate the efficacy of bevacizumab in the treatment of this disease.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.016 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".