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Record W3039424171 · doi:10.1002/jum.15390

Re: “Proposal for International Standardization of the Use of Lung Ultrasound for Patients With <scp>COVID</scp>‐19: A Simple, Quantitative, Reproducible Method”—Could Telementoring of Lung Ultrasound Reduce Health Care Provider Risks, Especially for Paucisymptomatic <scp>Home‐Isolating</scp> Patients?

2020· letter· en· W3039424171 on OpenAlexaff
Andrew W. Kirkpatrick, Jessica McKee

Bibliographic record

VenueJournal of Ultrasound in Medicine · 2020
Typeletter
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsFoothills Medical CentreUniversity of Calgary
Fundersnot available
KeywordsMedicineInterventional radiologyLung ultrasoundStandardizationCoronavirus disease 2019 (COVID-19)CitationLibrary scienceHealth careFamily medicineSurgeryUltrasoundPathologyRadiology

Abstract

fetched live from OpenAlex

To the Editor: We pay tribute to the efforts of Dr Soldati and colleagues,1 both for their front-line clinical care and in also making the extra efforts to study the potentially invaluable technique of point-of-care lung ultrasound (LUS), and especially their efforts to standardize and warehouse data to aid in research. The authors duly note that LUS could be used in a variety of global settings, including low- and middle-income countries, as well as during multiple times of disease progression, including the paucisymptomatic phase of coronavirus disease 2019 (COVID-19) pneumonia. We repeat our admiration of our colleagues, who took such great personal risks to obtain the ultrasound (US) images required to permit the development and subsequent validation of the LUS scoring system proposed by the authors. More than other imaging modalities, point-of-care US involves a return to the bedside by health care providers, who are more often becoming sick themselves. For infection control, the authors recommend that wireless US transducers wrapped in single-use plastic covers be used to physically contact the patient, although the smart device connected to the wireless US was often in proximity to the patient as well.1 We note that to actually conduct the examinations, one or even two health care providers were physically next to the patient. It was suggested that if one of these providers could be “distanced” from the patient, while controlling image acquisition, that this would reduce the operator dependence of US.1 We would humbly like to extend the authors' suggestions to propose that with current off-the-shelf informatics, both health care providers could potentially be physically isolated from the patient, thus reducing the exposure risk for any particular US examination to zero. This concept is most applicable to the majority of COVID-19–positive and potentially exposed patients, who will not develop severe respiratory failure requiring hospitalization and life support. As the authors note, LUS is easily able to detect interstitial lung disease, subpleural consolidations, and acute respiratory distress from any etiologic cause; thus in those with preexisting lung disease, the examination may be less useful in detecting early changes warning of worsening COVID pneumonia than in a young, previously healthy patient with completely normal lungs to begin with. For more than 15 years, we have confirmed that US-naïve nonphysicians can be remotely mentored by experts to obtain diagnostic-quality images2, 3 that can be remotely interpreted, using a treatment paradigm originally devised to support medical care in low earth orbit.4 Early chest computed tomography has been recommended for early detection of suspected COVID-19 pneumonia, with better sensitivity than a polymerase chain reaction.5 However, this is clearly impossible for home-isolated patients wondering whether to self-triage into the formal medical system. However, LUS may have comparable results to chest computed tomography with markedly reduced logistic challenges.6 We thus propose that most at-risk but otherwise well paucisymptomatic potential patients could have their screening augmented through remotely telementored LUS, following the standardized protocols and scores outlined by Soldati and colleagues.1 On the basis of previous studies, we believe that any other intelligent family member could be mentored to obtain interpretable images. In the case of a self-isolated individual with no family, a self-mentored examination would also be feasible, although in terms of technique, we would suggest that any mentored self-assessment begin from landmark 7 (excluding the back), as it would be unreasonable to expect average humans to be able to hold a transducer to their back. Finally, we declare that dedicated research examining the practicalities of mentored home LUS self-assessment be urgently studied, which we are planning to do. In a world in which health care providers seem to be inordinately at risk and with a potential crisis in personal protective equipment availability, anything else seems irresponsible.

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)0.011
metaresearch head score (Gemma)0.048
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.122
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.048
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0060.005
Scholarly communication0.0050.004
Open science0.0040.003
Research integrity0.1220.076
Insufficient payload (model declined to judge)0.0080.012

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 head0.073
GPT teacher head0.406
Teacher spread0.333 · 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
GenreCommentary

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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Citations3
Published2020
Admission routes1
Has abstractno

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