MétaCan
Menu
Back to cohort
Record W1453057671 · doi:10.3233/bme-151325

Experimental analysis of robot-assisted needle insertion into porcine liver

2015· article· en· W1453057671 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.

Bibliographic record

VenueBio-Medical Materials and Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsUniversity of Toronto
FundersFundamental Research Funds for the Central Universities
KeywordsBevelRobotControllabilityBiomedical engineeringProcess (computing)Computer scienceMaterials scienceLiver tissueDeformation (meteorology)SimulationArtificial intelligenceMechanical engineeringEngineeringMathematicsMedicineComposite material

Abstract

fetched live from OpenAlex

How to improve placement accuracy of needle insertion into liver tissue is of paramount interest to physicians. A robot-assisted system was developed to experimentally demonstrate its advantages in needle insertion surgeries. Experiments of needle insertion into porcine liver tissue were performed with conic tip needle (diameter 8 mm) and bevel tip needle (diameter 1.5 mm) in this study. Manual operation was designed to compare the performance of the presented robot-assisted system. The real-time force curves show outstanding advantages of robot-assisted operation in improving the controllability and stability of needle insertion process by comparing manual operation. The statistics of maximum force and average force further demonstrates robot-assisted operation causes less oscillation. The difference of liver deformation created by manual operation and robot-assisted operation is very low, 1 mm for average deformation and 2 mm for maximum deformation. To conclude, the presented robot-assisted system can improve placement accuracy of needle by stably control insertion process.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.561
Threshold uncertainty score0.435

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.017
GPT teacher head0.230
Teacher spread0.213 · 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