Custom three-dimensional printed splint for postoperative rehabilitation of a terrible triad elbow injury
Bibliographic record
Abstract
BackgroundWe present a case of using 3D printing to a bespoke elbow brace for a 48-year-old male following surgical repair of a terrible triad injury. As commercially available products were not suitable, and custom thermoplastic splinting was not available, the Rapid Innovation Unit in the University of Limerick was fabricated a splint using 3D printing. This splint was designed to facilitate rehabilitation, maintain a safe range of movement, and to be adjustable during the recovery period.MethodsA 3D Scanning kit was used to scan the patient’s arm. The scanning process uses high resolution non-contact mapping to sculpt an exact fit for the individual patient. Solidworks 3D modelling software was used to create the design for the brace. The completed design was then 3D printed using fused deposition modelling (FDM) and masked stereolithography apparatus (MSLA) printing methods.ResultsFollowing surgical intervention, the patient fitted with this model which was adjustable to facilitate increasing range during rehabilitation and did not require revision. At one year, the Disability of the ARM, Shoulder and Hand score was 14.16 and American Shoulder & Elbow Surgeons score was 92. The subjective elbow value was 80/100. The Quebec User Evaluation of Satisfaction with Assistive Technology (Quest) score was 56/60. The total time from measurement to final splint fitting was seven days and the total cost of materials/printing was €17.98. This excludes the cost of the 3D printer and labor.ConclusionThis case demonstrates the capacity of custom 3D printing to fabricate a splint for post operative rehabilitation in the setting of terrible triad injuries. We propose 3D printed splints have a potential role in the setting of complex elbow trauma where custom thermoplastic splinting is unavailable.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".