Evaluation of Collateral Ligament Location and Risk of Injury During Total Knee Replacement in Dogs
Bibliographic record
Abstract
Introduction: Injury to the medial collateral ligament (MCL) is the most common intraoperative complication in TKR. The purpose of this study was to define the locations of the collaterals relative to the femoral bone cuts in TKR. We hypothesized that CT-based planning risks violation of the collaterals (MCL > LCL). Materials and Methods: Paired pelvic limbs were harvested from 12 skeletally mature mixed-breed dogs (19–33.5 kg). The origins and insertions of the MCL and LCL were marked with titanium screws that could be visualized on CT. The footprints were also digitized directly using (1) a coordinate measuring machine, and (2) a white light scanner. The minimum distance from the ligament footprints to the femoral bone cuts was measured, with distances less than 5 mm being considered unsafe. Results: MCL and LCL footprints were consistently closer to the caudal femoral cut than to the distal femoral cut ( p < 0.001 for LCL, p < 0.05 for MCL). The footprints were within 5 mm of the caudal or distal ostectomy plane in 8 of 12 dogs (67%). Impingement of the MCL was seen in all eight cases, while only one case had contact with the footprint of the LCL. Discussion/Conclusion: Care must be taken to ensure that PSGs do not violate the MCL or LCL. Mapping of collateral ligament footprints, as performed in this study, can be used to create 3D point clouds that can be incorporated into CT-based planning, allowing surgeons to optimize implant positioning without increasing the risk of inadvertent collateral ligament injury. Acknowledgment None. Publication History Article published online: 15 July 2025 © 2025. Thieme. All rights reserved. Georg Thieme Verlag KG Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".