Three-Dimensional Printing in Hand Surgery: What Is New? A Systematic Review
Bibliographic record
Abstract
Aim: Preoperative planning and in-office patient education are essential elements of clinical management in patients afflicted with hand injuries. Three-dimensional (3D) printing aims to tackle these challenges by converting feedstock material into solid replicas. The purpose of this study was to review the clinical uses for 3D printing in hand surgery to identify approaches for delivering more personalized treatment strategies. Method: A systematic review was completed following PRISMA guidelines using Medline, Embase, and CINAHL databases, identifying studies published between 2013 and January 2025. A two-stage screening process, involving title, abstract, and full text reviews, was performed independently by two reviewers. Eligible studies included those involving patients with hand or wrist injuries (up to the distal radius) where 3D printing was utilized for diagnosis, surgical intervention, or rehabilitation. Results: The review included 751 patients (mean age: 38 years, range: 5–81 years) across 58 studies. The distal radius was the most commonly studied anatomical region (47%, N = 27), followed by the scaphoid (19%, N = 11). Key applications of 3D printing included preoperative planning (19%, N = 11), patient education (5%, N = 3), medical training (7%, N = 4), intra-operative assistance (38%, N = 22), splinting and casting (19%, N = 11), and prothesis and functional reconstruction (12%, N = 7). Conclusions: Despite its early stage of adoption in hand surgery, 3D printing has shown advantages, especially in enabling more personalized treatment strategies by improving intra-operative assistance, preoperative planning, and patient education. Further research is required to determine whether it positively affects postoperative outcomes, to calculate the cost–benefit ratio, and to compare its usage against standards of care.
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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 | 0.013 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".