A Re-exploration of the Use of Barbed Sutures in Flexor Tendon Repairs
Bibliographic record
Abstract
Flexor tendon repairs continue to improve thanks to advancements in suture material and technique. The role of barbed sutures in flexor tendon repairs has been previously investigated, but with the advent of a new material, interest in their use has been rekindled. We hypothesized that the use of modern barbed sutures will have comparable maximum tensile strength and 2-mm gapping strength to that of conventional sutures, allowing their use to theoretically decrease adhesions and tissue damage in flexor tendon repairs. Flexor tendon repairs were performed on a cadaver model using either 3-0 Ethibond (Ethicon, Inc, Somerville, New Jersey) (Kessler repair) or 2-0 Quill sutures (Angiotech, Vancouver, British Columbia, Canada ) (Kessler-Bunnell repair) and were biomechanically tested. The mode of failure for the Ethibond sutures was suture pullout 2 times and knot failure 18 of 20 times, while the Quill sutures failed entirely by pullout. Maximum load to failure was 34.7+/-5.4 N and 29.6+/-3.6 N for Ethibond and Quill, respectively. This was found to be statistically significant (P=.001). Tensile load at 2-mm gapping was 22.8+/-6.3 N and 22.2+/-4.0 N for Ethibond and Quill, respectively. No statistical significance was found (P=.723). This study helps substantiate the possible role of modern barbed sutures in flexor tendon repair. Additional biomechanical studies will need to be performed to further assess the use of barbed sutures in flexor tendon repair.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| 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".