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First-in-human, robotic-assisted neuroendovascular intervention

2020· article· en· W3010469249 on OpenAlexaff
Vítor Mendes Pereira, Nicole M Cancelliere, Patrick Nicholson, Ivan Radovanovic, Kaitlyn E Drake, John‐Michael Sungur, Timo Krings, Aquilla S Turk

Bibliographic record

VenueJournal of NeuroInterventional Surgery · 2020
Typearticle
Languageen
FieldMedicine
TopicIntracranial Aneurysms: Treatment and Complications
Canadian institutionsToronto Western HospitalUniversity Health Network
Fundersnot available
KeywordsMedicineNeurovascular bundleStentFluoroscopyRadiologyAneurysmMedical physicsSurgery

Abstract

fetched live from OpenAlex

Robotic-assisted technology has been used as a tool to enhance open and minimally invasive surgeries as well as percutaneous coronary and peripheral vascular interventions. It offers many potential benefits, including increased procedural and technical accuracy as well as reduced radiation dose during fluoroscopic procedures. It also offers the potential for truly “remote” procedures. Despite these benefits, robotic technology has not yet been used in the neuroendovascular field, aside from diagnostic cerebral angiography. Here, we report the first robotic-assisted, therapeutic, neuroendovascular intervention performed in a human. This was a stent-assisted coiling procedure to treat a large basilar aneurysm. All intracranial steps, including stent placement and coil deployment, were performed with assistance from the CorPath© GRX Robotic System (Corindus, a Siemens Healthineers Company, Waltham, MA, USA). This represents a major milestone in the treatment of neurovascular disease and opens the doors for the development of remote robotic neuroendovascular procedures.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.003

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.

Opus teacher head0.051
GPT teacher head0.287
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
Domainnot available
GenreEmpirical

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".

Quick stats

Citations142
Published2020
Admission routes1
Has abstractyes

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