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Record W2794730109 · doi:10.1109/biocas.2017.8325205

Implantable MICS-based wireless solution for bladder pressure monitoring

2017· article· en· W2794730109 on OpenAlexaff
A. Tantin, Antoine Letourneau, Mohamed Zgaren, Sami Hached, Ingelin Clausen, Mohamad Sawan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicUrinary Bladder and Prostate Research
Canadian institutionsPolytechnique Montréal
FundersNorges Forskningsråd
KeywordsTransceiverWirelessBase stationPressure sensorPower consumptionComputer scienceMicrosystemRemote patient monitoringPressure measurementBiocompatible materialResistive touchscreenEmbedded systemElectrical engineeringElectronic engineeringEngineeringBiomedical engineeringPower (physics)Materials scienceTelecommunicationsMedicineMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

This paper covers the design, development and prototyping of an implantable wireless bladder pressure monitoring system. The proposed device is an essential part of a novel health solution intended for patients with bladder dysfunctions. It allows the urologist to closely monitor the intravesical pressure and the patient to evacuate the collected urine on time, thus increasing his quality of life. The proposed implant includes a minimal number of components. Communication is performed through FDA-approved on-device RF transceivers, and an external base station. The base station integrates a Microsemi's ZL70120 module while the communication implant employs a ZL70123. Pressure measurement is made with custom built biocompatible Piezo-resistive pressure sensor probes operated in differential mode. The implant achieves a power consumption of 2.1 mW in sleeping mode while the consumption in operating mode is 18.6 mW. The communication range is around 2.5 meters. System design and experimental results are reported and discussed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.072
GPT teacher head0.385
Teacher spread0.313 · 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 source (direct Gemma or distilled Codex), 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

Citations11
Published2017
Admission routes1
Has abstractyes

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