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Record W6991174502

Evidence summary: What is the utility of lung sonography in COVID-19 pneumonia? [v1.0]

2020· other· en· W6991174502 on OpenAlexaboutno aff

Bibliographic record

VenueLenus, The Irish Health Repository (Dr Steevens Hospital Library) · 2020
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsLungUltrasoundPneumothoraxEconomic shortageLung ultrasoundPneumoniaModalities
DOInot available

Abstract

fetched live from OpenAlex

The potential utility of lung ultrasound (LUS) in COVID-19 patients is the subject of extensive investigation and debate. There is emerging evidence that LUS may be a useful aid in the triage and monitoring of COVID-19 pneumonia as it has high sensitivity for detecting pleural thickening, subpleural consolidation, and ground-glass opacity. It has the advantages of portability, point-of-care evaluation, reduced healthcare worker exposure, and repeatability during follow-up. The lack of ionising radiation is of particular relevance when imaging pregnant women and children11. It also has limitations - e.g. it cannot discern the chronicity of a lesion - and other imaging modalities may be required. In an Irish context, the following should also be taken into consideration so as not to exaggerate the potential utility of LUS: 1. While early reports suggest that LUS may be useful, the role of lung ultrasound in the evaluation of COVID-19 has not been established. 2. Lung ultrasound is heavily operator dependant and there is currently a shortage of relevant expertise in Ireland. 3. Lung ultrasound cannot detect ground-glass opacification in the lungs which is one of the signs of COVID-19 on standard imaging modalities. The mainstay of imaging evaluation in COVID-19 is chest radiography, supplemented by CT in certain clinical circumstances. Lung findings on CXR and CT are better described, are more objective, and are more likely to provide useful information to referring clinicians who are managing patients with this COVID-19. Further studies are needed to determine the added value of lung ultrasound in the management of COVID-19. 4. Lung ultrasound involves close contact between the patient and the sonographer for periods often exceeding 15 minutes, which increases the risk of transmission of infection to staff. These comments are subject to change in the light of new evidence which may emerge in this rapidly evolving area. Distinction should be drawn between lung ultrasound and pleural ultrasound: NCPR recognises that there is a definite established role for the latter in the evaluation of pleural effusions. 2020 guidance from the Canadian Society of Thoracic Radiology/Canadian Association of Radiologists1 suggests that LUS should not be used to diagnose or exclude COVID-19 pneumonia: “Although there is a growing identification of LUS patterns in COVID-19 pneumonia, the overlap with other causes of respiratory distress is unknown. Small outcome studies have been performed in patients with other causes of acute respiratory distress but are insufficient to support the use of LUS to contribute to or supersede established prognostic tools.” Other guidance, such as that from the Italian Network for Safety in Healthcare4 and the Polish Association of Epidemiologists and Infectiologists7, recommends the use of LUS for suspected COVID-19 pneumonia under certain circumstances: the Italian guidance recommends that chest ultrasound may be used if competencies are available and if strict disinfection protocols are followed after each examination4. The National Institutes of Health in the United States9 comment that: “The optimal pulmonary imaging technique for people with COVID-19 is yet to be defined. Initial evaluation may include chest x-ray, ultrasound or, if indicated, CT. Evaluation of patients with severe illness or pneumonia should include pulmonary imaging [CXR, US or if, indicated, CT] and ECG, if indicated.” A number of studies 13-15 note the utility of LUS in paediatrics; however, patient numbers in these reports are low. One commentator cautions against its use: “[given that] few children have COVID-19 and, particularly in winter, the prevalence of illnesses is high [with co-infection with COVID-19 in 40%], the pre-test probability of COVID-19 in children is low. That, together with the normal lung ultrasound on the patient we present here, convince us that ultrasound, though useful in many other scenarios, is a poor screening tool for COVID-19 in children”16. LUS remains an emerging area of interest in the context of COVID-19. Many authors highlight the need for larger prospective studies to evaluate the diagnostic and prognostic values, relevant findings, and discriminating features of LUS in the diagnosis and management of COVID-19. A number of pertinent trials have been registered and related studies continue to be released in pre-print format.

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)0.012
metaresearch head score (Gemma)0.115
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.031
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.115
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.007
Bibliometrics0.0050.004
Science and technology studies0.0010.002
Scholarly communication0.0050.004
Open science0.0040.002
Research integrity0.0080.006
Insufficient payload (model declined to judge)0.0310.006

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 head0.032
GPT teacher head0.298
Teacher spread0.266 · 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 designSystematic review
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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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