3D printing technology applied to orthosis manufacturing: narrative review
Bibliographic record
Abstract
Recently, three-dimensional (3D) printing technology has gradually been applied to the field of orthoses. This narrative review aimed to investigate the effect of 3D printed orthoses compared to conventional orthoses (non-3D printed orthoses). We searched MEDLINE for articles published up to July 27, 2020, and the main search phrases for identifying related articles were "3D printed orthosis", "3D printed orthoses", "3D printed braces", "3D printed splints", "3D printing orthosis", "3D printed orthoses", "3D printing braces" and "3D printing splints". We included articles that applied 3D printed orthoses to patients or healthy participants and excluded those not written in English, conference abstracts or presentations, and reviews. A total of 237 papers were identified, and qualifications were evaluated based on the title, abstract, and full text. A total of 22 articles were finally included in the analysis. The 3D printed orthoses showed similar or superior effects on biomechanical parameters and kinematic parameters such as wrist-hand function, wrist spasticity, arch height index, foot plantar pressure, and joint range of motion (ROM). In addition, 3D printed orthoses had high satisfaction and comfort compared to conventional orthoses. We believe that 3D printed orthoses can replace conventional ones, and they are expected to gain more popularity in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".