Technique description: Incisionless ultrasound-assisted biceps tenotomy in dogs
Bibliographic record
Abstract
Background: Bicipital tendinitis and/or tendinopathy is a common cause of forelimb lameness in dogs, particularly in larger and more active patients. Although conservative treatment aimed at resolving discomfort and preserving the tendon remains the primary therapeutic goal, in certain cases it is necessary to surgically transect the tendon to eliminate pain and lameness. Transection of the tendon can be performed by open arthrotomy, arthroscopically, or percutaneously using a scalpel blade. This paper examines the utility of a modified percutaneous approach using a hypodermic needle in place of a scalpel blade, under ultrasound-guided assistance. Aim: To develop and describe a surgical technique for performing a percutaneous biceps tenotomy using a hypodermic needle under ultrasound guidance. Method: The technique was piloted using the shoulders of 12 cadaver dogs initially and once developed, then applied to another 12 cadavers. The final procedure was performed on a total of 22 shoulders. Assessments were recorded on time to complete the procedure, completeness of bicipital tendon transection, and presence of any iatrogenic damage to associated joint structures. Results: Procedure time averaged fewer than 2 minutes. Complete transection was achieved in 20 out of 22 of the shoulders, with evidence of incomplete transection discernable by ultrasound imaging in the remaining two shoulders. One cadaver shoulder experienced iatrogenic damage secondary to incorrect hypodermic needle angulation. Conclusion: Percutaneous biceps tenotomy using a hypodermic needle is an efficient and straightforward procedure. The lack of a surgical incision makes it the least invasive technique devised so far. Ultrasound imaging allows the practitioner to assess the completeness of the transection increasing precision.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".