Minimally invasive ultrasound‐assisted cutting thread tenotomy of the deep digital flexor tendon in horses: An ex vivo study
Bibliographic record
Abstract
OBJECTIVE: To evaluate the feasibility and limitations associated with a minimally invasive ultrasound-assisted cutting thread technique for tenotomy of the deep digital flexor tendon (DDFT) in horses. STUDY DESIGN: Ex vivo study. SAMPLE POPULATION: Twenty cadaveric forelimbs. METHODS: Forelimbs were placed on a jig to mimic a standing semiflexed position and the midmetacarpal region was prepared to perform tenotomy of the DDFT using a percutaneous technique with a cutting thread. For that purpose, the thread was placed percutaneously around the DDFT (first dorsally and then palmarly) with the aid of a curved 20 gauge spinal needle. Tendon palpation/manipulation and ultrasonographic assessment assisted thread placement. Procedure time and skin puncture size were recorded. Limbs were then dissected to evaluate the degree of DDFT transection and the presence of any iatrogenic lesions. RESULTS: The DDFT was completely transected in all cases. Minor lesions of the superficial digital flexor tendon were found in 11/20 limbs and considered clinically irrelevant. However, the neurovascular bundle was damaged in 6/20 limbs (four limbs had nerve damage and two limbs had a nerve and either a palmar artery or vein damaged). The skin puncture hole sizes ranged from undetectable to 5 mm long. The average duration of the procedure was 7 min and 38 s (range: 4 min 56 s to 10 min 19 s). CONCLUSION: A DDFT tenotomy can be performed reliably with a percutaneous cutting thread technique. However, refinement of the technique is required to minimize iatrogenic damage. CLINICAL SIGNIFICANCE: The reported technique allows a DDFT tenotomy to be performed in a minimally invasive manner and has the potential to be clinically applicable.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".