Investigation of surgical adhesives for vocal fold wound closure
Bibliographic record
Abstract
OBJECTIVES: Surgical adhesives are increasingly used for vocal fold microsurgery to assist wound closure and reduce the risks of scar formation. Currently used vocal fold adhesives such as fibrin glue, however, have thus far not been found to promote wound closure or reduce scarring. The objectives of this study were to investigate the mechanical strength and the cytotoxicity of three commercially available adhesives (Glubran 2, GEM, Viareggio, Italy; BioGlue, CryoLife, Kennesaw, GA; and Tisseel, Baxter Healthcare, Deerfield, IL) for vocal fold wound closure. METHODS: Shear and tension tests were performed on 150 porcine larynges. The cytotoxicity of the adhesives to immortalized human vocal fold fibroblasts was investigated using neutral red uptake assays. RESULTS: The average shear adhesive strength for Tisseel, BioGlue, and Glubran 2 was 13.86 ± 5.03 kilopascal (kPa), 40.92 ± 17.94 kPa, and 68.79 ± 13.29 kPa, respectively. The tensile adhesive strength for Tisseel, BioGlue, and Glubran 2 was 10.70 ± 6.42 kPa, 34.27 ± 12.59 kPa, and 46.67 ± 12.13 kPa, respectively. The vocal fold cell viabilities in extracts of Tisseel, BioGlue, and Glubran 2 were 99.27%, 43.05%, and 1.79%, respectively. CONCLUSION: There was a clear tradeoff between adhesive strength and toxicity. The maximum failure strength in shear or tension of the three surgical adhesives ranked from strongest to the weakest was: 1) Glubran 2, 2) BioGlue, and 3) Tisseel. Tisseel was found to be the least toxic of the three adhesives, whereas Glubran 2 was the most toxic. LEVEL OF EVIDENCE: NA Laryngoscope, 129:2139-2146, 2019.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".