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Record W4385651572 · doi:10.1177/08850666231192047

Multiorgan Point-of-Care Ultrasound Assessment in Critically Ill Adults

2023· article· en· W4385651572 on OpenAlexaff
Hailey Hobbs, Scott J. Millington, Katie Wiskar

Bibliographic record

VenueJournal of Intensive Care Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsUniversity of OttawaUniversity of British ColumbiaOttawa HospitalQueen's University
Fundersnot available
KeywordsMedicineCritically illPoint of care ultrasoundProtocol (science)Intensive care medicinePoint of carePoint-of-care testingMedical physicsUltrasoundAlternative medicineNursingPathologyRadiology

Abstract

fetched live from OpenAlex

Traditional point-of-care ultrasound (POCUS) training highlights discrete techniques, single-organ assessment, and focused protocols. More recent developments argue for a whole-body approach, where the experienced clinician-ultrasonographer crafts a personalized POCUS protocol depending on specific clinical circumstances. This article describes this problem-based approach, focusing on common acute care scenarios while highlighting practical considerations and performance characteristics.

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)0.058
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.369
Threshold uncertainty score0.950

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.058
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.028
GPT teacher head0.389
Teacher spread0.361 · 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 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
Published2023
Admission routes1
Has abstractyes

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