Development of a Pericapsular Knee Desensitization Technique in Dogs: An Anatomical Cadaveric Study
Bibliographic record
Abstract
Regional anesthesia techniques targeting articular nerve branches offer promising avenues for managing articular pain. This study developed and compared the success rates of an ultrasound-guided versus a blind pericapsular knee desensitization (PKD) technique in canine cadavers. In Phase I, gross dissection and ultrasound evaluations were performed in eight limbs to characterize the anatomy of the medial (MAN), lateral (LAN), and posterior (PAN) articular branches of the saphenous, common fibular, and tibial nerves, respectively, and to identify suitable anatomical and ultrasonographic landmarks. In Phase II, ultrasound-guided and blind PKD injections of a dye solution were randomly performed in 10 cadavers (20 limbs), followed by dissection and histological assessment of staining accuracy. The ultrasound-guided technique achieved a significantly higher overall success rate (96.7%) than the blind technique (73.3%; p = 0.02). The MAN was successfully stained in 100% of ultrasound-guided and 50% of blind injections (p = 0.03), while the LAN and PAN were stained with high but comparable success. Parent nerve involvement was minimal for MAN and PAN but frequent for the common fibular nerve following LAN injections. Histological confirmation supported the anatomical findings, although PAN identification remained inconsistent. These results support the feasibility and increased precision of ultrasound-guided PKD, providing a foundation for further clinical evaluation.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".