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Record W2076732553 · doi:10.1002/rcs.158

Optimizing port placement for robot‐assisted minimally invasive cardiac surgery

2007· article· en· W2076732553 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInternational Journal of Medical Robotics and Computer Assisted Surgery · 2007
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsWestern University
FundersLondon Health Sciences Centre
KeywordsWorkspacePort (circuit theory)RobotComputer scienceSimulationInterface (matter)Robotic surgerySurgeryMedicineArtificial intelligenceEngineeringOperating systemMechanical engineering

Abstract

fetched live from OpenAlex

BACKGROUND: Proper placement of ports during robot-assisted endoscopic surgery is critical to the success of the procedure. In current practice, port placement methods do not consider the ability of the robot to manoeuvre the tools. METHODS: This paper proposes to choose the best port location such that the performance of the robot is maximized. The Global Conditioning Index (GCI) is used to optimize port placement when using the da Vinci((R)) surgical system during cardiac surgery. RESULTS: The results show that, due to a singularity at the remote centre of motion, higher performance is obtained the further away the port is from the workspace. When compared to the ports selected by an expert surgeon, our results show that it is possible to increase robot performance by at least 29% for the left arm of the robot. CONCLUSIONS: Selecting an adequate port location can improve robot performance and ensure that the instruments reach the surgical site.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.853
Threshold uncertainty score0.765

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.274
Teacher spread0.245 · 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