Biomechanical Analysis of Barbed Suture in Flexor Tendon Repair versus Conventional Method: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: The barbed suture technique uses newly developed materials for flexor tendon repair. In this study, the authors examine the effectiveness of using barbed sutures in flexor tendon repair compared with conventional methods. METHODS: A systematic literature review and meta-analysis was performed using MEDLINE, Embase, and Cochrane databases. Barbed suture and conventional suture methods were extracted as predictor variables, and maximum force, gap formation force, and cross-sectional area were extracted as outcome variables. Subgroup analyses were performed according to the source of suture materials and the number of strands. The Newcastle-Ottawa Scale was used to assess the quality of studies. Publication bias was evaluated using funnel plots. RESULTS: The search strategy identified 86 publications. After screening, 12 articles were selected for review. Barbed sutures are comparable in effectiveness to conventional methods in terms of maximum force, gap formation force, and cross-sectional area. In the subgroup analysis, barbed sutures also have comparable effects to conventional methods in terms of maximum force and gap formation force. CONCLUSIONS: The authors' meta-analysis found that the use of barbed sutures in flexor tendon repair was competitive compared to conventional methods in terms of maximum force and gap formation force. Long-term in vivo studies are needed to confirm these findings. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, V.
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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 | 0.014 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.024 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".