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Record W4405831128 · doi:10.1007/s42399-024-01769-0

A Wireless, Wearable Ultrasound for Assessing Left Ventricular Assist Device Hemodynamics: A Case Series

2024· article· en· W4405831128 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSN Comprehensive Clinical Medicine · 2024
Typearticle
Languageen
FieldEngineering
TopicMechanical Circulatory Support Devices
Canadian institutionsNOSM UniversityHealth Sciences North
Fundersnot available
KeywordsMedicineCardiologyDoppler effectInternal jugular veinUltrasoundInternal medicineWearable computerJugular veinCommon carotid arteryCardiac cycleDiastoleCarotid arteriesBiomedical engineeringRadiologyBlood pressureComputer sciencePhysics

Abstract

fetched live from OpenAlex

Abstract Optimizing left ventricular assist device (LVAD)–patient interaction is important. This is typically accomplished via an outpatient ramp test, monitored by echocardiography and/or invasive measures. We have developed a wireless, wearable Doppler ultrasound that we hypothesized would detect relatively small (i.e., ± 5%) changes in LVAD pump speed (Rpm ∆ ) with the patient in the upright position. From the wearable ultrasound, peak systolic velocity (PSV), end-diastolic velocity (EDV), total velocity time integral (VTI), Doppler pulsatility index, and the internal jugular vein spectrum were synchronously captured. Rpm ∆ was best reflected in the EDV and Doppler pulsatility index, whereas the PSV and total VTI showed variable responses. The internal jugular spectrum of one patient was consistent with high central venous pressure throughout the procedure. A wireless, wearable Doppler ultrasound synchronously insonating the common carotid artery and internal jugular vein provides novel and promising insights during LVAD Rpm ∆ .

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.614
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.050
GPT teacher head0.348
Teacher spread0.298 · 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