Advancing Prosthetic Care Access on the Thailand–Burma Border Through Open-Source Technology
Bibliographic record
Abstract
The ongoing civil war in Myanmar (Burma) has severely disrupted the country's health care system, forcing widespread displacement and creating a critical shortage of medical care and prosthetic devices for refugees and migrants along the Thai border. Prohibitive costs, resource shortages, and movement constraints severely limit access to essential health care and functional prosthetics in this region. This article details a collaboration between Queen's University and the Burma Children Medical Fund (BCMF), a nongovernmental organization (NGO) that established a 3-D-printed prosthetic program in 2019 using open-source technology. Since 2023, this collaboration has facilitated a three-year student mobility program, sending researchers to Thailand to enhance BCMF's existing initiative by addressing technical limitations and improving design autonomy. Using the development of a short transradial (below-elbow) prosthetic as a case study for this collaborative framework, the team employed low-cost 3-D scanning and computer-aided design (CAD) to modify an open-source prosthetic model for better suspension and fit. Our approach prioritized local capacity building by training local staff in CAD to ensure design autonomy and implementing filament dryers to mitigate humidity-related printing errors. From 2019 to 2024, the program provided free prostheses to 76 individuals, including five recipients of the customized below-elbow design. Pilot evaluations using the Orthotics Prosthetics Users Survey (OPUS) indicated positive results, with recipients reporting high levels of functionality and satisfaction, and no reported adverse effects. Ultimately, this work demonstrates the feasibility of a framework for academic-NGO partnerships that leverages adaptable, open-source technology to deliver equitable prosthetic care in conflict-affected and resource-constrained settings.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".