MétaCan
Menu
Back to cohort
Record W1507344904 · doi:10.1109/iembs.2006.260543

Friction-Assisted Magnetic Holding of an Ingestible Capsule for Esophageal pH and Impedance Monitoring

2006· article· en· W1507344904 on OpenAlexaff
José Luis González, Daniel Sadowski, Martin P. Mintchev

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsUniversity of Alberta HospitalAlberta Hospital EdmontonUniversity of Calgary
Fundersnot available
KeywordsMagnetWirelessComputer scienceElectrical impedanceElectromagnetic shieldingCatheterBiomedical engineeringMechanical engineeringSimulationAutomotive engineeringMaterials scienceElectrical engineeringEngineeringSurgeryTelecommunicationsMedicine

Abstract

fetched live from OpenAlex

24-hour catheter-based ambulatory pH and impedance monitoring is an essential tool for diagnosing esophageal disorders. However, catheter-based monitoring systems are uncomfortable and interfere with normal activities of the patient. To overcome these disadvantages, different wireless monitoring systems have been proposed. However, efficient ways to position and hold wireless capsules are lacking. Currently there is a need to develop safe and reliable methods to hold an esophageal wireless monitoring system in position for 24 hours. Friction-assisted magnetic holding is proposed as an alternative to conventional holding techniques. Permanent magnet and electromagnet designs with the required characteristics to achieve this task were computer-designed and simulated. The size and power requirements of the magnets were considered. Simulation results were verified using laboratory experiments. Permanent neodymium magnets offered the best performance for the intended application. The obtained results show the feasibility of friction-assisted magnetic holding for esophageal monitoring. Improvements to the thread design, friction enhancing pins, magnetic shielding and encapsulation methods are necessary for in vivo testing.

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)0.000
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 score0.213
Threshold uncertainty score0.549

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 head0.010
GPT teacher head0.217
Teacher spread0.207 · 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

Citations3
Published2006
Admission routes1
Has abstractyes

Explore more

Same topicWireless Power Transfer SystemsFrench-language works237,207