A Systematic Review on the Use of Fibrin Glue for Peripheral Nerve Repair
Bibliographic record
Abstract
BACKGROUND: Although Tisseel (Baxter, Deerfield, Ill.) was introduced over 30 years ago, the literature remains scant regarding its use and efficacy in peripheral nerve repairs. The goal of this systematic review was to summarize current literature on this topic and discuss differences in clinical outcome between the use of fibrin glue and conventional suturing methods for the repair of peripheral nerves. METHODS: A comprehensive electronic literature search was run in the following databases: Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, MEDLINE, CINAHL, and EMBASE. Articles were classified into three categories: animal, human, and cadaveric studies. RESULTS: Sixteen articles were included in the final analysis (kappa = 0.78). The most consistent outcome measure of the studies assessed was histopathology, which was evaluated in nine of 16 studies. This was followed closely by biomechanics, which were evaluated in eight of 16 studies. Histopathological studies demonstrated less significant granulomatous inflammation in the fibrin groups as well as better overall axonal regeneration, fiber alignment, and recovery of nerve conduction velocities. Animal and cadaveric studies demonstrated no significant differences in stiffness and peak load at failure between microsuture and fibrin groups. CONCLUSIONS: Although the majority of the reviewed studies employed animal models, most indicated that the performance of fibrin glue was equal, if not superior, to that of microsuturing when repairing peripheral nerves. Overall, many authors reported that fibrin glue was a quicker and easier modality to use than microsuture repair. There is, however, not a single well-controlled human trial assessing the efficacy of fibrin glue in relation to that of suturing techniques.
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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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".