Image‐based design and 3D‐metal printing of a rat hip implant for use in a clinically representative model of joint replacement
Bibliographic record
Abstract
The aim of this study was to obtain micro-computed tomography derived measurements of the rat proximal femur, to create parameterized rat hip implants that could be surgically installed in a clinically representative small animal model of joint replacement. The proximal femoral anatomy of N = 25 rats (male, Sprague-Dawley, 390-605 g) was quantified. Key measurements were used to parameterize computer-aided design models of monoblock rat femoral implants. Linear regression analysis was used to determine if rat hip dimensions could be predicted from animal weight. A correlation analysis was used to determine how implants could be scaled to create a range of sizes. Additive manufacturing (3D printing) was used to create implants in medical-grade metal alloys. Linear regressions comparing rat weight to femoral head diameter and neck-head axis length revealed a significant nonzero slope (P < .05). Pearson's correlation analysis revealed five significant correlations between key measurements in the rat femur (P < .05). Implants were installed into both cadaveric and live animals; iterative design modifications were made to prototypes based on these surgical findings. Animals were able to tolerate the installation of implants and were observed ambulating on their affected limbs postoperatively. Clinical significance: We have developed a preclinical rat hip hemiarthroplasty model using image-based and iterative design techniques to create 3D-metal printed implants in medical-grade metal alloys. Our findings support further development of this model for use as a low-cost translational test platform for preclinical orthopaedic research into areas such as osseointegration, metal-on-cartilage wear, and periprosthetic joint infection.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".