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Record W4292615793 · doi:10.1002/jum.16088

New International Guidelines and Consensus on the Use of Lung Ultrasound

2022· review· en· W4292615793 on OpenAlexaff
Libertario Demi, Frank Wolfram, Catherine Klersy, Annalisa De Silvestri, Virginia Valeria Ferretti, Marie Müller, Douglas L. Miller, Francesco Feletti, Marcin Wełnicki, Natalia Buda, Agnieszka Skoczylas, Andrzej Pomiećko, Domagoj Damjanović, Robert Olszewski, Andrew W. Kirkpatrick, Raoul Breitkreutz, Gebhart Mathis, Gino Soldati, Andrea Smargiassi, Riccardo Inchingolo, Tiziano Perrone

Bibliographic record

VenueJournal of Ultrasound in Medicine · 2022
Typereview
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsUniversity Hospital FoundationFoothills Medical CentreUniversity of Calgary
Fundersnot available
KeywordsMedicineStatement (logic)Consensus conferenceMultidisciplinary approachLung ultrasoundMedical physicsUltrasoundRadiologyLaw

Abstract

fetched live from OpenAlex

Following the innovations and new discoveries of the last 10 years in the field of lung ultrasound (LUS), a multidisciplinary panel of international LUS experts from six countries and from different fields (clinical and technical) reviewed and updated the original international consensus for point-of-care LUS, dated 2012. As a result, a total of 20 statements have been produced. Each statement is complemented by guidelines and future developments proposals. The statements are furthermore classified based on their nature as technical (5), clinical (11), educational (3), and safety (1) statements.

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)0.016
metaresearch head score (Gemma)0.021
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 score0.016
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.021
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0100.007
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0040.003
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0060.004

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 head0.311
GPT teacher head0.464
Teacher spread0.153 · 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

Citations319
Published2022
Admission routes1
Has abstractyes

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