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Role of Point of Care Ultrasonography in Patients with COVID-19 Associated Acute Kidney Injury

2022· review· en· W4210386071 on OpenAlexvenueno aff
Daniel W. Ross, Zubair Hasan

Bibliographic record

VenuePOCUS Journal · 2022
Typereview
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAcute kidney injuryCoronavirus disease 2019 (COVID-19)NephrologyUltrasonographyIntensive care medicineInternal medicineKidney diseaseComplicationSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakDiseaseRadiologyPathologyInfectious disease (medical specialty)Outbreak

Abstract

fetched live from OpenAlex

The severe acute respiratory virus covariate-2 (SARS CoV-2) that causes Corona Virus Disease 2019 (COVID-19) has affected more than 194 million people worldwide and has attributed to or caused more than 4 million deaths. Acute kidney injury (AKI) is a common complication of COVID-19. Point of care ultrasonography (POCUS) can be a useful tool for the nephrologist. POCUS can be used to elucidate the cause of kidney disease and then also help to manage volume status. Here, we review pearls and pitfalls of using POCUS to manage COVID-19 associated AKI with special attention to kidney, lung, and cardiac ultrasound.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.963
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.028
GPT teacher head0.367
Teacher spread0.338 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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