Lower Limb Salvage Using Patent-Specific 3D-Printed Titanium Cage Following Severe Left Ankle Traumatic Partial Amputation: A Pediatric Case Report
Bibliographic record
Abstract
BACKGROUND: Patients with large bony defects of the ankle who wish to avoid amputation have limited surgical intervention options for limb salvage. Each of these interventions are technically complex and present significant risk for complications. The use of a patient-specific 3D-printed titanium cage in conjunction with a tibiotalocalcaneal (TTC) arthrodesis using a retrograde nail is another management option. This case adds to the scarce published literature on this technique. CASE PRESENTATION: This report presents the case of a 16-year-old female who suffered a traumatic partial amputation of her left distal lower extremity following an all-terrain-vehicle accident that resulted in a 10.0 × 10.0 cm skin laceration and a 5-cm subsegmental bony loss of the distal tibia. She was successfully treated using a patient-specific 3D-printed titanium truss cage in conjunction with a TTC arthrodesis using a retrograde nail. CONCLUSIONS: The decision to amputate or attempt limb salvage in a severely injured lower limb is still a topic of active debate. However, literature has shown that patients who undergo limb salvage surgery have better psychological health outcomes and equivalent functional outcomes as patients who have undergone amputation. Therefore, research on techniques that optimize and advance limb salvage surgery is needed. As the numerous potential benefits and limitations of patient-specific 3D-printed implants are assessed throughout the field of orthopedics, further research and cost-analysis will be required. Cases such as the one presented add to the limited existing literature of patient-specific 3D-printed implant for treatment of large distal lower extremity bony defects. LEVELS OF EVIDENCE: Level V (Case Report).
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".