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Record W4407834270 · doi:10.1097/ruq.0000000000000706

Lung Ultrasound for the Assessment of Patients With Acute Respiratory Failure

2025· review· en· W4407834270 on OpenAlexaff
Kelly Greenwall, Kevin Kobes, Brian Buchanan, Christopher Fung

Bibliographic record

VenueUltrasound Quarterly · 2025
Typereview
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineLung ultrasoundAcute respiratory failureIntensive care medicineLungRespiratory systemUltrasoundRespiratory failureRadiologyInternal medicineMechanical ventilation

Abstract

fetched live from OpenAlex

ABSTRACT: Lung ultrasound has become a standard practice in acute care as an adjunct to the physical exam, providing valuable insights to guide clinical decision making at the point of care. Lung ultrasound can rapidly uncover anatomic detail, help resolve undifferentiated respiratory failure, and delineate equivocal findings on standard plain film without the need for transport to access additional diagnostic imaging. In the following review, basic concepts of lung ultrasound are reviewed including its role in detecting and assessing a variety of common problems.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.026
GPT teacher head0.381
Teacher spread0.355 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2025
Admission routes1
Has abstractyes

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