A systematic review of the Woven EndoBridge device—do findings in pre-clinical animal models compare to clinical results?
Bibliographic record
Abstract
BACKGROUND: The Woven Endobridge (WEB) is designed to treat intracranial wide-neck bifurcation aneurysms, preventing subarachnoid hemorrhage. The translational value of animal models used for WEB device testing is unknown. With this systematic review, we aim to identify the existing animal models used in testing the WEB device and compare the efficacy and safety outcomes to those of prospective clinical studies. METHODS: This study was funded by ZonMw: project number 114024133. A comprehensive search was performed in PubMed and in EMBASE via the Ovid interface. The following exclusion criteria were used: 1) not an original full-length research paper, 2) not an in vivo animal study or a human study, 3) no WEB implantation, 4) if in humans: not a prospective study. The SYRCLE risk of bias tool (animal studies) and the Newcastle-Ottawa quality assessment scale for cohort studies (clinical studies) were used to assess risks of bias. A narrative synthesis was performed. RESULTS: Six animal studies and 17 clinical studies met the inclusion criteria. The rabbit elastase aneurysm model was the only animal model used to assess WEB device performance. Safety outcomes were never reported in animal studies. Efficacy outcomes were more heterogeneous in animal studies than in clinical studies, which could be due to limited external validity of the animal models in terms of aneurysm induction and dimensions. Both animal and clinical studies were predominantly single-arm studies, and were at unclear risk of several types of bias. CONCLUSIONS: The rabbit elastase aneurysm model was the only pre-clinical animal model used to assess WEB device performance. Safety outcomes were not evaluated in animal studies and could therefore not be compared to clinical outcomes. Efficacy outcomes were more heterogeneous in animal studies than in clinical studies. Future research should focus on improving methodology and reporting in order to draw accurate conclusions on the performance of the WEB device.
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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 | 0.036 | 0.155 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.011 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".