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Record W2774108301 · doi:10.1109/iecon.2017.8216552

A novel sensor for real-time measurement of force and torque of colonoscope

2017· article· en· W2774108301 on OpenAlexaff
Changyuan Zheng, Zhiqin Qian, Kang Zhou, Hao Liu, Dongyuan Lv, Wenjun Zhang

Bibliographic record

VenueIECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society · 2017
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsColonoscopyComputer scienceTorqueWork (physics)PerforationBowel perforationArtificial intelligenceComputer visionSimulationMedicineSurgeryEngineeringMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

Colonoscopy is widely used in the diagnosis and treatment of colorectal diseases due to its minimal invasiveness, convenience and efficiency. However, it has two problems: bowel perforation and looping. In order to overcome these problems, it is necessary to get the information of the force and posture of the distal end of the colonoscope in the colonoscopy procedure. Elsewhere, we have reported an approach to have a sensor on the hose of colonoscope, which is outside the human body, to infer the force information at the distal end which is inside the human body, via a kinetic model. This paper presents a work on developing such a sensor. The goal of this work is to improve the accuracy of the senor while maintaining its low cost.

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 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)0.001
metaresearch head score (Gemma)0.001
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 score0.009
Threshold uncertainty score0.549

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.097
GPT teacher head0.307
Teacher spread0.210 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations6
Published2017
Admission routes1
Has abstractyes

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