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Record W2890261932 · doi:10.1097/mnh.0000000000000453

Point-of-care ultrasound in end-stage kidney disease

2018· review· en· W2890261932 on OpenAlexaff
William Beaubien‐Souligny, Josée Bouchard, André Denault

Bibliographic record

VenueCurrent Opinion in Nephrology & Hypertension · 2018
Typereview
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsHôpital du Sacré-Cœur de MontréalCentre Hospitalier de l’Université de MontréalMontreal Heart Institute
Fundersnot available
KeywordsEnd-stage kidney diseaseStage (stratigraphy)MedicineEnd stage renal diseaseEnd pointKidney diseasePoint of careUltrasoundIntensive care medicineDiseaseInternal medicineRadiologyPathologyComputer scienceBiology

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Following the miniaturization of ultrasound devices, point-of-care ultrasound (POCUS) has been proposed as a tool to enhance the value of physical examination in various clinical settings. The objective of this review is to describe the potential applications of POCUS in end-stage renal disease patients (ESRD). RECENT FINDINGS: With basic training, the clinician can perform pulmonary, vascular, cardiac, and abdominal POCUS at the bedside of ESRD patients. Pulmonary ultrasound can be used to quantify pulmonary congestion and for the differential diagnosis of dyspnea. Ultrasound of the inferior vena cava combined with simple cardiac ultrasound can be used to promptly investigate the mechanism of hemodynamic instability. Vascular ultrasound can be used for troubleshooting of arteriovenous fistula problems and for catheter installation. Multiple potential applications of POCUS in the ESRD population are reviewed, including areas of future research. SUMMARY: Acquiring basic skills in POCUS may improve patient care through the rapid identification of threats, improved diagnostic abilities for common symptoms, and safer procedures. The adoption of POCUS in undergraduate, internal medicine and nephrology training curriculums will likely lead to a gradual introduction of this technology in the care of ESRD patients.

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)0.003
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 score0.851
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.121
GPT teacher head0.416
Teacher spread0.295 · 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
Published2018
Admission routes1
Has abstractyes

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