Cyanoacrylate glue as a novel skin substitute in periocular skin excisions: case series and literature review
Bibliographic record
Abstract
OBJECTIVE: Despite the widespread use of cyanoacrylate glue (CA) as an alternative for wound closure, its potential as a sole skin substitute material in periocular skin surgery remains unexplored. The primary objective was to determine the viability of CA as a sole skin substitute in periocular skin surgery after excision. DESIGN: Single-centre retrospective observational case series. METHODS: All patients were treated at the McGill University Health Centre from August 2023 to November 2023, where CA served as the sole skin substitute material after periocular skin excision. RESULTS: Three female and one male patient, with a mean age of 75 years, received treatment with CA after skin excision for both cancerous and benign skin lesions. Specifically, histopathology revealed 2 cases of basal cell carcinoma, 1 case of squamous cell carcinoma in situ, and 2 benign lesions. The skin defects after excision ranged from 4 × 3 mm to 15 × 30 mm. No complications were observed between CA graft insertion and final skin re-epithelization. Complete re-epithelization was achieved in all patients at final follow-up without evidence of excessive skin contraction. CONCLUSIONS: This study presents a novel approach by using CA as a skin substitute material in periocular skin excisions. Its liquid form allows for easy application and conforms well to irregular wound surfaces. CA may offer economic advantages, including lower material cost and shorter surgical operating times, compared with traditional skin substitutes. Further research is needed to comprehensively evaluate CA's role as a skin substitute in periocular skin reconstruction.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| 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".