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Record W4311148084 · doi:10.1016/j.cjca.2022.11.015

Emerging Applications of Extracardiac Ultrasound in Critically Ill Cardiac Patients

2022· review· en· W4311148084 on OpenAlexaffvenue
Martin Girard, Jean Deschamps, Saad Razzaq, Nicolas Lavoie, André Denault, William Beaubien‐Souligny

Bibliographic record

VenueCanadian Journal of Cardiology · 2022
Typereview
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsMontreal Heart InstituteMcGill UniversityCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsMedicineUltrasoundRadiologyCritically illModalitiesContrast-enhanced ultrasoundElastographyModality (human–computer interaction)Intensive care medicineMedical physicsArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

Point-of-care ultrasound has evolved as an invaluable diagnostic modality and procedural guidance tool in the care of critically ill cardiac patients. Beyond focused cardiac ultrasound, additional extracardiac ultrasound modalities may provide important information at the bedside. In addition to new uses of existing modalities, such as pulsed-wave Doppler ultrasound, the development of new applications is fostered by the implementation of additional features in mid-range ultrasound machines commonly acquired for intensive care units, such as tissue elastography, speckle tracking, and contrast-enhanced ultrasound quantification software. This review explores several areas in which ultrasound imaging technology may transform care in the future. First, we review how lung ultrasound in mechanically ventilated patients can enable the personalization of ventilator parameters and help to liberate them from mechanical ventilation. Second, we review the role of venous Doppler in the assessment of organ congestion and how tissue elastography may complement this application. Finally, we explore how contrast-enhanced ultrasound could be used to assess changes in organ perfusion.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.988
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.042
GPT teacher head0.358
Teacher spread0.316 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations6
Published2022
Admission routes2
Has abstractyes

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