Arthroscopic Long Digital Extensor Tenodesis: Technique and Outcome in a Dog
Bibliographic record
Abstract
Abstract Avulsion of the long digital extensor (LDE) tendon is an uncommon cause of pelvic limb lameness in the dog. Surgical exploration is recommended with reattachment of the tendon and the avulsed fragment of bone using a small screw in lag fashion. In more chronic cases, the bone fragment can be removed, and the tendon attached by suture or staple to the proximolateral tibia. Arthroscopic tenodesis of the LDE has not been previously reported in the literature. A 6-year-old male neutered Labrador Retriever was presented for evaluation of a left pelvic limb lameness that was localized to the stifle. Avulsion of the LDE tendon was diagnosed based on palpation and radiographs. Arthroscopic assessment of the joint was performed. The origin of the LDE was freed with the use of a mechanical 3.0-mm shaver and an arthroscopic punch. The freed end was then exteriorized from the lateral portal and mineralized tissue removed. The tendon end was captured using no. 2 braided suture loop using a SpeedWhip technique. A 4.75-mm SwiveLock® polyetheretherketone anchor was utilized to fasten the tendon in the proximal groove of the LDE. The dog returned to dock diving by 16 weeks postsurgery at the preinjury level of performance and remained free of lameness at 30 months postoperatively. Arthroscopic long digital tendon tenodesis is a feasible surgical option in canine patients.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".