Custom 3D-Printed Total Talar Prostheses Restore Normal Joint Anatomy Throughout the Hindfoot
Bibliographic record
Abstract
BACKGROUND:: Third generation total talar prostheses (TTPs) are viable options for talar avascular necrosis (AVN) in the absence of neighboring joint pathology. The use of modern three-dimensional (3D) printing allows the production of custom implants that exactly mimic the patient's anatomy. The aim of this study is to determine the accuracy of 3D printing in reproducing a synthetic talus and, in doing so, restoring more normal anatomical relationships. We hypothesize that this mode of replication will restore and maintain normal radiographic alignment of the ankle, subtalar, and forefoot joints in the setting of talar AVN. METHODS:: A retrospective analysis was performed on all patients undergoing TTP implantation for the treatment of talar AVN between 2016 and 2017. Radiographic measurements were taken preoperatively and postoperatively to determine native talar dimensions, TTP implant dimensions, and the corresponding radiographic alignment about the forefoot, hindfoot, and ankle. RESULTS:: A total of 14 patients were identified in our cohort. Talar arc length and width were not found to be significantly changed; however, talar height was significantly increased with use of TTP. Five alignment dimensions were measured, of which, only talar tilt angle was significantly changed. Instances of Meary's angle correction were observed in cavus and planus foot deformity. CONCLUSION:: As a proof of concept, 3D-printed TTP was successful in restoring talar height and talar tilt in the setting of AVN. Additionally, the procedure maintained normal alignment in nonpathological joints. TTPs, based on our cohort, are a viable option to restore more normal anatomical alignment. LEVELS OF EVIDENCE:: Level IV: Case series.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".