SURGICAL OUTCOMES OF 3D PRINTED MUSCULOSKELETAL METAL IMPLANTS: A SYSTEMATIC REVIEW
Bibliographic record
Abstract
Introduction: Additive manufacturing, also known as 3D printing (3DP), is becoming increasingly available to surgeons throughout the world due to recent advancements in technology. 3D printing can produce complex free-form structures that would be impossible using conventional subtractive manufacturing. This offers the possibility to create implants that are better suited to the irregular anatomic shapes found in the human body. The present study aims to examine the surgical outcomes associated with the use of 3D printed metal implants and uncover the value of 3D printing in musculoskeletal surgery. Methods: A systematic review of published literature was performed in June 2017 using the PRISMA protocol. Online bibliographic databases such as MEDLINE, Embase, Scopus, CINAHL, and Cochrane were used to identify studies involving surgical implantation of 3D printed metal implants in musculoskeletal surgery. References from relevant studies were scanned for additional articles. Two reviewers independently screened results. Full-text articles were analyzed for eligibility. A total of 24 studies were included for data abstraction. Results were collected and qualitatively analyzed. Results: Of the 25 articles included, there were 17 case reports, 4 case series, 2 retrospective cohorts and 3 prospective cohorts. Of these articles, the majority of 3DP was done with electron beam melting (EBM) with Ti6Al4V. Orthopaedic, neurosurgical, plastic, and maxillofacial surgery articles were included in the review. All studies concluded that 3D printed implants had favourable post-operative outcomes. Some advantages included the reduction of operative time, improved osseointegration through custom implant porosity, improved fixation, decreased stress shielding, better cosmetic appearance, improved functional outcome, and limb salvage. Additional cost and time required to design and print the implants were reported as potential drawbacks to 3D printing. Discussion/Conclusions. The applications of 3D printing in musculoskeletal surgery are promising and have the potential to alter future surgical practice. However, there is a lack of quality research in the literature assessing the use of 3D printed implants. Further research is needed to evaluate the use of 3D printing in musculoskeletal surgery to understand its potential effects on surgical practice.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".