Patient‐specific 3D‐printed shelf implant for the treatment of hip dysplasia: Anatomical and biomechanical outcomes in a canine model
Bibliographic record
Abstract
A solution for challenging hip dysplasia surgery could be a patient-specific 3D-printed shelf implant that is positioned extra-articular and restores the dysplastic acetabular rim to normal anatomical dimensions. The anatomical correction and biomechanical stability of this concept were tested in a canine model that, like humans, also suffers from hip dysplasia. Using 3D reconstructed computed tomography images the 3D shelf implant was designed to restore the radiological dysplastic hip parameters to healthy parameters. It was tested ex vivo on three dog cadavers (six hips) with hip dysplasia. Each hip was subjected to a biomechanical subluxation test, first without and then with the 3D shelf implant in place. Subsequently, an implant failure test was performed to test the primary implant fixation. At baseline, the dysplastic hips had an average Norberg angle of 88 ± 3° and acetabular coverage of 47 ± 2% and subluxated at an average of 83 ± 2° of femoral adduction. After adding the patient-specific shelf implants the dysplastic hips had an average Norberg angle of 122 ± 2° and acetabular coverage of 67 ± 3% and subluxated at an average of 117 ± 2° of femoral adduction. Implant failure after primary implant fixation occurred at an average of 1330 ± 320 Newton. This showed that the patient-specific shelf implants significantly improved the coverage and stability of dysplastic hips in a canine model with naturally occurring hip dysplasia. The 3D shelf is a promising concept for treating residual hip dysplasia with a straightforward technology-driven approach; however, the clinical safety needs to be further investigated in an experimental proof-of-concept animal study.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".