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Record W1986338685 · doi:10.1109/embc.2012.6346653

On the use of smart stents for monitoring in-stent restenosis

2012· article· en· W1986338685 on OpenAlexaff
Kamyar Keikhosravy, Arash Zargaran-Yazd, Shahriar Mirabbasi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectrical and Bioimpedance Tomography
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRestenosisStentComputer scienceMedicineRadiology

Abstract

fetched live from OpenAlex

In angioplasty with stent placement, re-narrowing of the artery within the stent site may occur due to the body's natural response to "heal" the stented area. This re-narrowing, also known as "in-stent restenosis", usually occurs within 6 months after surgery. To monitor and diagnose in-stent restenosis, passive telemonitoring using smart stents has been already proposed. In this paper, we present a feasibility study and advocate the use of an alternative method, namely active telemonitoring, which uses an integrated circuit embedded on the smart stent. Electromagnetic simulations and in-vitro measurements are presented to find the suitable range of frequency to wirelessly transfer power to the active device embedded on the smart stent. Furthermore, the range of induced power levels are simulated and experimentally verified.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.329
Threshold uncertainty score0.140

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 head0.067
GPT teacher head0.240
Teacher spread0.173 · 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 designObservational
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

Citations19
Published2012
Admission routes1
Has abstractyes

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