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Enregistrement 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 sur OpenAlexaff
Andrew W. Kirkpatrick, Jessica McKee

Notice bibliographique

RevueJournal of Ultrasound in Medicine · 2020
Typeletter
Langueen
DomaineMedicine
ThématiqueUltrasound in Clinical Applications
Établissements canadiensFoothills Medical CentreUniversity of Calgary
Organismes subventionnairesnon disponible
Mots-clésMedicineInterventional radiologyLung ultrasoundStandardizationCoronavirus disease 2019 (COVID-19)CitationLibrary scienceHealth careFamily medicineSurgeryUltrasoundPathologyRadiology

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,011
score de la tête « metaresearch » (Gemma)0,048
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,122
Score d'incertitude au seuil0,060

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0110,048
Méta-épidémiologie (sens strict)0,0010,002
Méta-épidémiologie (sens large)0,0020,002
Bibliométrie0,0010,001
Études des sciences et des technologies0,0060,005
Communication savante0,0050,004
Science ouverte0,0040,003
Intégrité de la recherche0,1220,076
Charge utile insuffisante (le modèle a refusé de juger)0,0080,012

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,073
Tête enseignante GPT0,406
Écart entre enseignants0,333 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations3
Publié2020
Routes d'admission1
Résumé présentnon

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