The Utility of Point-of-Care Ultrasound in Detecting Distal Forearm Buckle Fractures in Paediatric Patients
Notice bibliographique
Résumé
Abstract BACKGROUND: Distal forearm fractures are common paediatric injuries. The use of plain radiographs is almost universal for diagnosis but this technology can be painful and time consuming. It is well known that children’s pain in the emergency department (ED) is both under-recognized and suboptimally managed. Point-of-care ultrasound (POCUS) has demonstrated good accuracy in detecting cortical disruption in adults but test characteristics in the most common paediatric fracture type (buckle or torus fractures) have not been explored. POCUS may also lead to quicker assessment and be associated with less pain and improved caregiver satisfaction compared to plain radiographs. Our results may provide support an alternative diagnostic strategy for centres where plain radiographs are not readily accessible and where severe pain is an issue. OBJECTIVES: The objectives of this study were to: (i) evaluate the diagnostic accuracy POCUS in detecting suspected non-angulated distal forearm fractures in children presenting to the ED compared to AP and lateral plain film x-rays of the forearm, (ii) determine the length of time required to complete a POCUS evaluation for a suspected distal forearm non-angulated fracture in children in the ED, (iii) explore the differences in caregiver satisfaction with POCUS evaluation compared to plain radiography, and (iv) explore differences in associated pain between POCUS and plain radiography. DESIGN/METHODS: This was a prospective cohort study designed to test the hypothesis that POCUS provides comparable sensitivity and specificity to plain radiography and is associated with less pain and greater caregiver satisfaction. We included children aged 4-17 years who presented to the paediatric ED with a suspected non-angulated distal forearm fracture based on a typical mechanism of injury. The patient underwent an x-ray and a POCUS evaluation of the affected region by a Canadian Emergency Ultrasound (CEUS)-trained physician who was blinded to the x-ray results. Caregivers were asked to complete a satisfaction questionnaire using a five-point Likert scale and children were asked to complete the Faces Pain Scale – Revised (FPS-R) reflecting discomfort associated with the diagnostic modality. The primary POCUS diagnosis made by the physician was compared to the x-ray diagnosis made by the staff paediatric radiologist. All POCUS images were independently interpreted by a second expert POCUS sonographer blinded to the original POCUS interpretation, x-ray, and final diagnosis. The primary outcomes were sensitivity of POCUS and pain score using the FPS-R. RESULTS: Eighty-five participants were enrolled, of whom 33 (39%) sustained a buckle fracture. The mean (SD) age of the participants was 11 (3.4) years and 52 (61%) were male. Sensitivity and sensitivity of POCUS for detecting any cortical disruption was 97% [95% CI: 84.2, 99.9] and 96% [95% CI: 86.8, 99.5], respectively. Agreement (kappa) between sonographers was 0.74 [95% CI: 0.64, 0.87] representing substantial agreement. POCUS was associated with significantly lower mean (SD) pain scores compared to plain radiography [2.3 (2.5) vs 3.6 (3.0), p < 0.01]. The median (IQR) time to perform POCUS was significantly lower that plain radiography [68 (42) vs 1200 (1440) seconds, p < 0.01]. POCUS was associated with comparable mean caregiver satisfaction compared to plain radiography [4.7 (0.7) vs 4.4 (1.0), p = 0.15]. CONCLUSION: This prospective study of POCUS for non-angulated distal forearm injuries in children suggests that POCUS is associated with excellent sensitivity and specificity for the detection of cortical disruption. Furthermore, POCUS is associated with significantly less pain and procedure time compared to plain radiography. The results suggest that POCUS could be a useful diagnostic strategy in resource-limited settings for children with suspected forearm injuries and has the added benefit of lower associated pain.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».