Essential Requirements and Relevant Technologies for Load-Bearing 3D-Printed Transtibial Prosthetic Sockets and Their Components: State-of-the-Art Review
Bibliographic record
Abstract
Background: The manufacture of load-bearing prosthetic lower limb sockets is traditionally reliant on skilled technicians working with qualified clinicians to create bespoke solutions. While this approach is effective and, in some situations, necessary, the appeal of a sustainable, efficient, and digitalized production solution cannot be ignored. The focus of additive manufacturing (AM) is typically on low-weight-bearing prostheses, which can be misleading for clinics attempting to adopt AM solutions for clientele with weight-bearing or activity-level needs. Objective: This review aims to offer readers a way to approach AM for load-bearing requirements as opposed to non-load-bearing counterparts. The use cases of AM for the production of load-bearing transtibial prosthetic sockets and components are reviewed to highlight current trends, protocols, and standings. Methods: By reviewing publications from the past 25 years, this state-of-the-art review highlights the key requirements and technologies relevant for load-bearing transtibial prosthetic sockets specifically. Results: The most commonly used AM solutions for commercial use, such as selective laser sintering and binder jetting through Multi Jet Fusion, are outlined. As these solutions are most often paired with the structural testing standard International Organization for Standardization 10328, their relevance for evaluating the strength and durability of lower limb sockets is also discussed. Clinician and technician experiences of digitalized ways of working within the prosthetic industry for load-bearing applications are outlined. Conclusions: Observations of adoption barriers of AM solutions are brought to light, focusing on clinician and technician education, skill set, exposure to innovative technologies, and trust in the regulation of digital processes in a clinical and technical environment.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".