Objective performance metrics in human robotic neuroendovascular interventions: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: The objective of this scoping review is to review the available information on objective performance metrics used during robotic neuroendovascular intervention procedures on humans. INTRODUCTION: Robotic neuroendovascular intervention is defined as any endovascular procedure within the vasculature of the central nervous system with the assistance of a robotic system for diagnostic or therapeutic procedures. Robotic systems are described as a 2-component system consisting of a patient-side mechanical robot, and a separate operator control station. Robotic neuroendovascular intervention is a growing field and there is a need to establish objective performance metrics for furthering evidence-based reporting of the literature. INCLUSION CRITERIA: This scoping review will consider all studies involving humans that utilize robotic neuroendovascular intervention. We will consider all types of studies, reports, and reviews as well as gray literature. Studies will be included if they describe the use of an objective performance metric during robotic neuroendovascular intervention. This review is not limited to a particular country or health care system, and will consider all study designs, regardless of their rigor or language. METHODS: Utilizing a 3-step framework as a guide, we will perform a systematic search in Embase, Cochrane Library, and MEDLINE. Available literature from inception to the present will be considered. Studies will be independently screened according to the inclusion criteria by 2 reviewers based on title, abstract, and full text. Data will be extracted, sorted, and presented in both a narrative summary as well as table and diagram based on the objective of the scoping review.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.125 | 0.128 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.013 | 0.018 |
| Bibliometrics | 0.027 | 0.018 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.011 | 0.006 |
| Insufficient payload (model declined to judge) | 0.050 | 0.012 |
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".