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Enregistrement W7161815853 · doi:10.82308/30583

Development and implementation of a novel web-based trauma and operating theater registry in Tanzania: The Amber Database initiatives

2024· dissertation· en· W7161815853 sur OpenAlexaboutno aff
Cherinet Osebo

Notice bibliographique

Revuenon disponible
Typedissertation
Langueen
DomaineMedicine
ThématiqueGlobal Health and Surgery
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésCommissionHealth careMajor traumaService (business)Global healthNarrative reviewService providerTrauma care

Résumé

récupéré en direct d'OpenAlex

Surgical conditions arising from trauma burdens pose massive challenges to understanding the real global health burden. This is particularly true in resource-limited settings, where well-structured data infrastructures are lacking, highlighting the essential need for trauma surveillance systems. Trauma registries are comprehensive, prospective data repositories for trauma patients, covering demographic information, injury details, surgical procedures, care, and outcomes. These registries are crucial in trauma care systems, enhancing patient outcomes. The significant surgical burden resulting from trauma-related fatalities in low- and middle-income countries (LMICs) emphasizes the increasing interest in digitizing trauma data infrastructures in these regions. Despite the feasibility of several traditional paper-based pilot trauma registries in LMICs, achieving long-term sustainability remains challenging. This thesis proposes three interrelated objectives to comprehensively address healthcare structures in resource-limited settings. [Manuscript I] The study was initiated with a comprehensive narrative literature review to assess the progress of Global Surgery 2030 initiatives in resource-limited settings. This assessment aimed to measure progress in surgical services since the inception of the Lancet Commission on Global Surgery (LCoGS 2030) and the National Surgical, Obstetrics, and Anesthesia Plans (NSOAPs). The LCoGS stated six key global surgery metrics, including access to essential trauma/surgical services, surgical workforce, surgical volume, surgical outcomes tracking system, finances, and infrastructure. The review revealed a significant gap between current surgical capacity and the LCoGS 2030 recommendations in resource-limited settings. The key message is that improving surgical care in these settings requires comprehensive approaches, including increasing surgical infrastructures and workforces, implementing insurance plans, and strengthening surgical service tracking systems. [Manuscript II] To address the need for an increased surgical/trauma workforce in resource-limited settings, the study suggests, among other solutions, targeted training programs. To address these gaps, an eco-friendly and sustainable course called Trauma and Disaster Team Response (TDTR) was developed by McGill's Centre for Global Surgery (CGS) and delivered at the Muhimbili Orthopedic Institute (MOI) in Tanzania. The course, taught mostly by Tanzanian instructors with limited CGS support, was designed to empower medical professionals to provide critical care for trauma/surgical services. The study aimed to evaluate the TDTR’s effectiveness and practicality in advancing professionals' skills and patient outcomes in this setting. Following the implementation of the TDTR course, significant improvements were observed in the skills, teamwork, and confidence of trauma care providers, as well as in clinical outcomes. Positive feedback from trainees and local communities provided clear evidence of the course's impact, reflecting its success in improving patient outcomes and clinicians' skills and teamwork of trauma care providers. This success has prompted considering expanding the course throughout Tanzania and similar settings and has provided potential solutions to address gaps in the skilled workforce.[Manuscript III and IV] Finally, WHO recognizes the significant global burden of trauma, particularly in underdeveloped healthcare systems in LMICs. Standardization of trauma registries is critical to saving resources and improving trauma care. Digitization of these registries is particularly important in regions with high injury burdens, such as Tanzania, for mapping injury epidemiology, benchmarking clinical guidelines, and injury prevention. The Amber database, a novel web-based infrastructure for trauma and operating room data, was developed and implemented at the Tanzanian MOI from July 13 to August 23, 2023, to assess its feasibility. Trained staff prospectively collected data from the MOI emergency department and operating rooms, totaling nearly 2400 data: 1097 traumatic patients and 1300 operated patients. Positive feedback from key stakeholders at MOI validated the feasibility of implementing such a digitized platform in a Tanzanian setting. Since its introduction, Amber has quickly become a routine MOI's medical recording system, allowing data to be used for targeted education, quality improvement, and health policy formulation. In summary, the thesis vividly outlines challenges in global surgical access, underscores the necessity of advancements in trauma education, and proposes the implementation of eco-friendly trauma and operating room data infrastructures, Amber database in Tanzanian resource-limited settings to improve healthcare structures. This platform, launched in July 2023 and reported until August, recorded data for 2400 patients on both trauma and operating room details, growing to nearly 5000 by December 2023, showcasing the feasibility and success at the MOI, Tanzania

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)0,021
score de la tête « metaresearch » (Gemma)0,029
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: aucune
GenreSignal candidat: Méthodes · Signal consensuel: aucune
Score de désaccord entre enseignants0,021
Score d'incertitude au seuil0,110

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0210,029
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0040,003
Études des sciences et des technologies0,0010,001
Communication savante0,0030,004
Science ouverte0,0020,004
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0070,002

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 Opus0,033
Tête enseignante GPT0,363
Écart entre enseignants0,330 · 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
GenreMéthodes

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
Publié2024
Routes d'admission1
Résumé présentoui

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