The Use of 3D Printing Technology in Rehabilitation for Adults Living with Neurological Conditions: A Scoping Review (Preprint)
Bibliographic record
Abstract
BACKGROUND Neurological conditions can significantly impact how someone functions and participates in daily life. Neurorehabilitation plays a key role in improving motor recovery for people with neurological conditions. Three-dimensional (3D) printing has emerged as a promising rehabilitation tool, but little is known on how it is used for the rehabilitation of adults living with neurological conditions. OBJECTIVE We aimed to provide a comprehensive overview of how 3D printing is currently used in neurorehabilitation and precisely, explore how it is used to improve motor recovery for adults with neurological conditions in higher and lower-middle-income countries. METHODS We conducted a scoping review following Joanna Briggs Institute (JBI) guidelines. After searching three databases, MEDLINE, Web of Science, and Nursing and Allied Health Premium, two independent reviewers screened and selected English-language studies involving adults (18+) published between 2019 and 2024 to capture the most recent advancements in this field. We extracted and documented relevant information around the neurological condition, motor recovery outcomes, and types of 3D printing application used across different countries using a modified JBI extraction form. We synthesized the findings narratively with tabular support. RESULTS After screening 2,752 titles and abstracts and 103 full texts, we included 13 studies based on our inclusion criteria. All included studies were conducted in upper-middle-income or high-income countries, most studies focused on stroke (n=9), followed by spinal cord injury (n=2), Parkinson's disease (n=1), and central nerve disease (n=1). 3D printed rehabilitation tools included orthotics (n=7 upper extremities, n=3 lower extremities), an exoskeleton (n=1, upper extremities), a modular assistive hand device (n=1, upper extremities), and an insole (n=1, LE). Nine studies targeted upper extremities rehabilitation measured by the Action Research Arm Test, Active range of motion, Box and Block Test, Fugl-Meyer Assessment, Modified Ashworth Scale, Manual Function Test, Range of motion, Toronto Rehabilitation Institute-Hand Function Test; and four lower extremities rehabilitation measured by the 10-meter walking test, Anteroposterior Ground Reaction Force Analysis, Barthel Index, Tinetti scale, RehaWatch system, and the GaitWatch system. CONCLUSIONS 3D printing technology used as rehabilitation tools have demonstrated significant potential in improving upper and lower motor recovery for people with certain neurological conditions in high-middle income countries. Future research should explore the implementation feasibility and effectiveness of these technologies across different neurological conditions, and income settings, particularly in low- and lower-middle-income countries.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.014 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".