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Enregistrement W4411433180 · doi:10.1016/j.ard.2025.06.628

POS1278 VALIDATION OF HANDHELD ULTRASOUND DEVICES FOR POINT OF CARE USE IN RHEUMATOLOGY: ANALYSIS FOR JOINTS AND NAILS

2025· article· en· W4411433180 sur OpenAlexaffabout
Ümmügülsüm Gazel, Shailja C. Shah, Marie Maguin, Rohan Machhar, P Leclerc, Lihi Eder, Gurjit S. Kaeley, Sahin Aydin

Notice bibliographique

RevueAnnals of the Rheumatic Diseases · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueSystemic Sclerosis and Related Diseases
Établissements canadiensWomen's College HospitalNovartis (Canada)University of Ottawa
Organismes subventionnairesnon disponible
Mots-clésMedicineRheumatologyMobile deviceInternal medicinePoint of careMedical physicsPathologyWorld Wide Web

Résumé

récupéré en direct d'OpenAlex

Background: Ultrasonography (US) has experienced a rapid evolution in rheumatology. Despite many advantages being shown repeatedly, several barriers persist and stand in the way of a wider use of US in rheumatology, equipment cost being an important one. Hand-held US technology promises to take this cost down substantially. However, before it can be specifically used for rheumatology, its performance needs to be validated against gold-standard devices for key interventions. Objectives: We aim to test the concurrent validity of a handheld US device versus a gold-standard device to detect characteristic features of healthy and rheumatic joints (i.e., anatomical structures and vascular flow). Methods: Adult patients with peripheral PsA presenting with at least one tender and swollen joint were included. Each patient had consecutive US examinations using a handheld (Clarius Mobile Health Inc, HD3 L20 and L15 scanners) and a gold standard US device (GE LogicE9- E10) for detecting synovitis, erosions and nail disease. B mode and power Doppler images were saved for each site and lesion. Every image was given a unique identifier number at the end. A random order slide show was conducted for scoring, irrespective of the machine used or the anatomical site or patient assessed, to ensure blindness. Scoring was done using previously validated methods. Results: Thirty patients were recruited (n of scanned sites for synovitis=720; erosions=120; and nail disease=60). Patients had a mean±SD age of 56.6±11.2, with a median (IQR) disease duration of 7(2-12.25) years. 50% were female. On the day of the US, the median (IQR) tender joint count was 6(2-6) and swollen joint count was 2(1-2). The agreement between the handheld and gold standard devices for all elementary lesions assessed is shown in the table (Table 1). For detecting synovitis, the L15 and L20, had a kappa of 0.488 and 0.455 compared to the GE machine, respectively, with absolute agreement rates of 76.4-73.5%. For detecting the intrasynovial Doppler signals, the L15 had moderate agreement (kappa: 0.420, absolute agreement: 90.3%); and L20 had fair agreement (kappa: 0.367, absolute agreement: 90.9%). For erosions, there was moderate agreement with the L15 (kappa: 0.570, absolute agreement: 82.7%) and substantial agreement with L20 (kappa: 0.619, absolute agreement: 85.6%). The nail lesions were compared with L20, which showed a substantial agreement to detect the loss of trilaminar appearance (kappa: 0.643, absolute agreement: %87.9) and substantial agreement to detect nail bed vascularity (kappa: 0.663, absolute agreement: 89.5%). Conclusion: In this analysis, the handheld US devices show moderate-substantial agreement to detect synovitis, nail lesions and erosions. In Doppler activity, fair agreement was detected in the L20 device, while moderate agreement was detected in L15 device. These results encourage the use of handheld US devices for wider use. REFERENCES: NIL . Table 1The frequency, kappa and percent absolute agreement of elementary lesions on all three devices.Hand HeldL20 ScannerL15 ScannerGE LogicE9ErosionAbs-entPres-entKappa% Absolute agreementAbs-entPres-entKappa% Absolute agreementAbsent75110.61985.63340.57082.7Present520510Nail- loss of tri-laminar appearanceAbsent4230.64387.9Present49Nail DopplerAbsent820.66389.5Present443Synovitis-B ModeAbsent201510.45573.5154160.48876.4Present671265266Synovial DopplerAbsent384100.36790.924080.42090.3Present30141912 Acknowledgements: NIL . Disclosure of Interests: Seyyid Acikgoz: None declared, Ummugulsum Gazel: None declared, Suharsh Shah Full-time employee at Novartis Pharmaceuticals Canada Inc, Marie Maguin Full-time employee at Novartis Pharmaceuticals Canada Inc, Rohan Machhar Full-time employee at Novartis Pharmaceuticals Canada Inc, Patrick Leclerc Novartis employee, Lihi Eder Abbvie, Pfizer, UCB, Fresenius, Novartis, J&J, Abbvie, Pfizer, UCB, Novartis, Eli Lilly, BMS, Moonlake, J&J, Abbvie, Pfizer, UCB, Novartis, Eli Lilly, J&J, Fresenius Kabi, Gurjit Kaeley Research grants from Novartis, Gilead/ Galapagos, BMS,Janssen, Abbvie, Sibel Aydin received payment or honoraria for lectures, presentations, speaker's bureaus, manuscript writing, or educational events from Abbvie, Jannsen, Novartis, Pfizer, and UCB, received payment or honoraria for lectures, presentations, speaker's bureaus, manuscript writing, or educational events from Abbvie, Jannsen, Novartis, Pfizer, and UCB, stock options in Clarius, received consultant's fees from Abbvie, Celgene, Eli Lilly, Novartis, Pfizer, Sanofi, and UCB, received grants or contracts from Abbvie, Celgene, Eli Lilly, Jannsen, Novartis, Pfizer, Sanofi, and UCB. © The Authors 2025. This abstract is an open access article published in Annals of Rheumatic Diseases under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Neither EULAR nor the publisher make any representation as to the accuracy of the content. The authors are solely responsible for the content in their abstract including accuracy of the facts, statements, results, conclusion, citing resources etc.

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)0,009
score de la tête « metaresearch » (Gemma)0,015
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,009
Score d'incertitude au seuil0,048

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0090,015
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,001
Communication savante0,0010,000
Science ouverte0,0010,001
Intégrité de la recherche0,0010,000
Charge utile insuffisante (le modèle a refusé de juger)0,0070,003

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 Opus0,043
Tête enseignante GPT0,327
Écart entre enseignants0,284 · 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'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

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

En bref

Citations0
Publié2025
Routes d'admission2
Résumé présentoui

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