<i>Ex‐Vivo</i>Evaluation of a Modified Teno Fix<sup>®</sup>Device Repair Pattern Versus a 3‐Loop Pulley for Repair of Equine Flexor Tendons
Bibliographic record
Abstract
OBJECTIVE: To compare the load to ultimate failure, load to a 2 mm gap, mode of failure, and gap at failure (mm) of the Teno Fix(®) (TF) to the 3-loop pulley (3LP) for repair of equine tendon lacerations. The use of 4TF devices versus 5TF devices on load to ultimate failure and load to a 2 mm gap was also compared. STUDY DESIGN: Ex vivo biomechanical study; unbalanced incomplete block (horse) design. SAMPLES: Cadaveric equine forelimb superficial digital flexor tendon (n = 20 paired tendons). METHODS: Ten tendon pairs were selected randomly and repaired with a 3LP and 4TF pattern, and 10 tendon pairs repaired with a 3LP and 5TF pattern. Load to ultimate failure, load to a 2 mm gap, mode of failure, and gap at failure were obtained using materials testing. RESULTS: The 3LP had a significantly greater ultimate load to failure (P < .001, respectively) and a significantly higher load to a 2 mm gap than both TF repairs (P < .001, respectively). The most frequent mode of failure was suture pull-out for the 3LP and anchor pull-out for the TF. Gap at failure was significantly larger in the 3LP than both TF repairs (P < .001). The ultimate load to failure was significantly higher for the 5TF than the 4TF (P = .004) but there was no significant difference in load to a 2 mm gap, or gap at failure between both TF repairs (P = .11, P = .15, respectively). CONCLUSIONS: Neither TF repair was stronger than the 3LP in load to ultimate failure and load to a 2 mm gap. Addition of a fifth TF device significantly increased the load to ultimate failure but did effect the load to a 2 mm gap over the 4TF.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".