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Record W3213644318 · doi:10.1109/ius52206.2021.9593456

In vitro and clinical demonstration of relative velocity measurements with the Flopatch™: A wearable Doppler ultrasound patch

2021· article· en· W3213644318 on OpenAlexaff
Chelsea E. Munding, Christopher Acconcia, Mai Elfarnawany, Joseph K. Eibl, Pietro Verrecchia, Patrick Leonard, Aaron Boyes, Zhen Yang, Rony Atoui, Christine Démoré

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicHemodynamic Monitoring and Therapy
Canadian institutionsHealth Sciences NorthSunnybrook Hospital
Fundersnot available
KeywordsPulsatile flowPeristaltic pumpBiomedical engineeringDoppler effectImaging phantomFlow velocityVolumetric flow rateUltrasoundMedicineFlow measurementMaterials scienceFlow (mathematics)AcousticsNuclear medicinePhysicsCardiologyRadiologyMechanics

Abstract

fetched live from OpenAlex

A hands-free continuous wave (CW) Doppler ultrasound patch has been developed for noninvasive assessment of carotid stroke volume. The patch enables easy, continuous monitoring, which is particularly useful in a fast-paced setting such as the intensive care unit. In vitro validation of the measurement of relative change in velocity was performed with a flow phantom for a maximum velocity range of 10–150 cm/s, showing good agreement, with average errors of 2.1% and 3.9% for constant and pulsatile flow, respectively. In a pilot clinical demonstration, a recording was made during on-pump coronary artery bypass surgery. The recorded maximum velocity trace was compared with the peristaltic pulsation frequency, showing that the patch tracked the large changes in pump flow rate. Notably, in the seconds following the changes in pump flow, clear differences were observed, consistent with cerebral autoregulation. Further investigations are ongoing into other metrics of carotid flow and stroke volume, and into the clinical utility of the device for assessing fluid responsiveness.

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)0.000
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 score0.158
Threshold uncertainty score0.164

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.040
GPT teacher head0.321
Teacher spread0.281 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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