Canine Stifle Biomechanics Associated With a Novel Extracapsular Articulating Implant Predicted Using a Computer Model
Bibliographic record
Abstract
OBJECTIVE: To evaluate the influence of the Simitri Stable in Stride™ extracapsular articulating implant (EAI) on canine stifle biomechanics in the cranial cruciate ligament (CrCL)-deficient stifle using a 3-dimensional (3D) quasi-static rigid body canine pelvic limb computer model simulating the stance phase of gait. STUDY DESIGN: Computer simulations. ANIMALS: Five-year-old neutered male golden retriever (33 kg). METHODS: The EAI was implemented in a previously developed 3D CrCL-deficient canine pelvic limb computer simulation model. Ligament loads, relative tibial translation, and relative tibial rotation were determined and compared to the CrCL-intact and CrCL-deficient stifle. RESULTS: The EAI significantly increased peak caudal cruciate and medial collateral ligament loads, significantly changed when peak lateral collateral ligament load occurred, and did not significantly affect peak patellar ligament load compared to the CrCL-intact stifle. Compared to the CrCL-deficient stifle, peak caudal cruciate, lateral collateral, and medial collateral ligament loads significantly decreased in the EAI-managed stifle. Despite decreased peak caudal cruciate ligament loading, the EAI-managed stifle generated local maxima that exceeded those of the CrCL-deficient stifle at various intervals of stance. Peak relative tibial translation and rotation significantly decreased in the EAI-managed stifle compared to the CrCL-deficient stifle. CONCLUSION: Model-predicted stifle biomechanics differed after EAI system application in the CrCL-deficient stifle, but were not restored to that of the CrCL-intact stifle.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".