Development and implementation of a novel web-based trauma and operating theater registry in Tanzania: The Amber Database initiatives
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".