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Record W2039152362 · doi:10.1088/0964-1726/15/6/021

Design and analysis of a micromachined piezoelectric sensor for measuring the viscoelastic properties of tissues in minimally invasive surgery

2006· article· en· W2039152362 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

VenueSmart Materials and Structures · 2006
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsConcordia University
Fundersnot available
KeywordsViscoelasticityPiezoelectricityInvasive surgeryBiomedical engineeringMaterials sciencePiezoelectric sensorAcousticsMechanical engineeringSurgeryEngineeringComposite materialMedicinePhysics

Abstract

fetched live from OpenAlex

In this paper, the design, analysis and fabrication of a micromachined piezoelectric endoscopic tactile sensor to determine the properties of tissues in minimally invasive surgery is presented. The viscoelastic Kelvin model is employed for tissue characterization. A closed form expression is derived to express the relationship between the force ratio, compliance and the equivalent viscous damping of the tissue. The designed sensor uses a PVDF film as its sensing element. The sensor consists of rigid and compliant elements which are mounted on the tip of an endoscopic surgical grasper tool. The relative force between adjacent parts of the contact object is used to measure the viscoelastic properties experimentally.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score0.217

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 head0.016
GPT teacher head0.202
Teacher spread0.186 · 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