Transarterial Therapy for Neuroendocrine Tumors: Protocol for a Systematic Review and Network Meta-analysis
Bibliographic record
Abstract
ABSTRACT Introduction Neuroendocrine neoplasms are a form of tumor that develops from a variety of neuroendocrine cell types and is most commonly found in the gastrointestinal tract. As the blood from the GI tract drains into the liver, it frequently metastasizes there. Intraarterial therapies, such as bland transarterial embolisation, conventional transarterial chemoembolisation, drug-eluting beads transarterial chemoembolisation, and transarterial radioembolization with yttrium-90 are advanced methods for limiting the burden of neuroendocrine liver metastasis tumors. Currently, there is no study that compares all of these methods. Our goal is to compare the efficacy and harm of each of transarterial therapies for stage IV neuroendocrine neoplasms by conducting this systematic review and network meta-analysis. Methods and Analysis We will search electronic databases (MEDLINE, Cochrane, Embase, Scopus, ClinicalTrials.gov ) from their inception to November 2022 to find all studies that compare survival and clinical finding between any two of the following intraarterial interventions: bland transarterial embolisation, conventional transarterial chemoembolisation, drug-eluting beads transarterial chemoembolisation, and transarterial radioembolization, the latter with yttrium-90. We will include randomized controlled trials (RCT), cluster-RCTs, and observational studies. We will investigate selected articles regarding our primary outcomes (Overall survival, Progression-free survival, death) and also our secondary outcomes (post embolization syndrome, hepatic abscesses, hepatic failure, gallbladder necrosis, non-target embolization). We will also search trial registries and the references of included studies to identify any additional published or unpublished studies for possible inclusion. Risk of bias will be assessed using the tools developed by Cochrane (i.e., the Cochrane RoB 2 tool for RCTs and cluster RCTs, and ROBINS-I for observational studies). We will conduct frequentists network meta-analyses, and we will use the Confidence in Network Meta-Analysis tool to assess the confidence in the evidence for the primary outcomes. Ethics and Dissemination We seek to publish our findings through peer review with a high impact, and readers will have access to the data gathered in this study. Because the underlying research is based on systematic evaluations of available data, it does not require approval by an ethical review board. The systematic review and meta-analysis’s findings will also be presented at various conferences and seminars. Strengths and Limitations This meta-analysis can be used to compare different types of intraarterial therapies for NELM, which can serve as a guide for choosing the best treatment option for non-surgical candidates. This research will lay the groundwork for future clinical trials comparing these therapy options, which can guide clinicians with a greater degree of certainty. Variations in specific therapy, like differences in medications used for intervention, may not be considered and may affect this comparison.
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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 | 0.037 | 0.069 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.023 | 0.036 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.043 | 0.003 |
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".