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Enregistrement W3116653520 · doi:10.1097/jpo.0000000000000349

An International, Multicenter Field Trial Comparison Between 3D-Printed and ICRC-Manufactured Transtibial Prosthetic Devices in Low-Income Countries

2020· article· en· W3116653520 sur OpenAlexaff
Matt Ratto, Joshua Qua Hiansen, Jennifer Marshall, Moses Kaweesa, Jennan Taremwa, Thearith Heang, Sisary Kheng, Odom Teap, Donald Mchihiyo, Ruth Onesmo, Baraka Moshi, Violet Mwaijande, Jerry Evans

Notice bibliographique

RevueJPO Journal of Prosthetics and Orthotics · 2020
Typearticle
Langueen
DomaineEngineering
ThématiqueProsthetics and Rehabilitation Robotics
Établissements canadiensMPB Technologies & Communications (Canada)University of Toronto
Organismes subventionnairesnon disponible
Mots-clés3d printedMedicineClinical trialDeveloping countryInformed consentAmputationEthics committeePopulationBiomedical engineeringSurgeryEnvironmental health

Résumé

récupéré en direct d'OpenAlex

ABSTRACT Introduction The gap between the needs of individuals with amputation and access to prosthetists in low-income countries (LICs) is significant. Training new personnel to bridge this gap would exceed the current output of all prosthetic and orthotic programs globally. Strategies are needed to increase the productivity of existing prosthetists in order to serve more patients. Emerging technologies such as 3D scanning, modeling, and printing have been investigated for their ability to decrease the manufacturing time for prosthetic devices; however, few studies have compared the efficacy of 3D-printed devices to traditionally manufactured (e.g., International Committee of the Red Cross [ICRC]) devices. Studies that previously compared these two methods were limited by low population size and restricted timeframes. The purpose of this study was to gather evidence comparing the efficacy of 3D-printed and ICRC transtibial prostheses in large patient populations in LICs over time. Materials and Methods A total of 61participants between the ages of 5 and 25 completed this study's 8-week trial. Participants were recruited from four clinical sites in Uganda, Tanzania, and Cambodia. Ethics approval was obtained from each of the four clinical sites before study initiation. Consent was obtained from each participant before study enrolment. The participants' residual limbs were 3D scanned by local prosthetists using hand-held 3D scanners. Prosthetists digitally rectified the 3D scanned models using Canfit and NiaFit 3D modeling software. The rectified models were fabricated using 3D printers. 3D-printed devices were lined with foam liners and coupled to standard ICRC pylons and feet. Participants used the 3D-printed sockets for 4 weeks, then returned to the clinic to complete a 28-question Likert scale questionnaire, assessing their experiences with their 3D-printed devices. Surveys were based on the Prosthesis Evaluation Questionnaire. Participants were then given a new transtibial prosthetic device manufactured using traditional ICRC methods and instructed to use this device for 4 weeks. They then returned to the clinic to complete the questionnaire as aforementioned. Responses from both surveys were assessed using a two-tailed Student t -test ( P < 0.05). Results Data from the Tanzania Training Centre for Orthopaedic Technologists (n = 10) indicated that their users rated ICRC devices significantly higher in categories measuring stability, including ability to walk, walking up steep slopes and stairs, walking on slippery surfaces, overall fit, comfort while standing, and texture of the device. In contrast, participant data from Comprehensive Rehabilitation Services in Uganda (n = 25), Cambodian School of Prosthetics and Orthotics (n = 10), and Comprehensive Community Based Rehabilitation in Tanzania (n = 16) showed no significant differences across all measured outcomes. Conclusions This is the first study to compare and contrast the efficacy of 3D-printed and ICRC transtibial prosthetic devices across geographic locations in LICs with a large study population. Results demonstrate that, in general, 3D-printed devices were rated comparably to ICRC. This result was consistent at three of four clinical trial sites. Further studies will be required to elucidate the rating differences observed at the fourth site.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,634
Score d'incertitude au seuil0,747

Scores Codex et Gemma par catégorie

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

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.

Tête enseignante Opus0,012
Tête enseignante GPT0,268
Écart entre enseignants0,256 · 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 tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
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

Citations16
Publié2020
Routes d'admission1
Résumé présentoui

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