Fur apposition technique—a new technique for simple laceration closure in small animals (dogs and cats): a pilot study
Bibliographic record
Abstract
Objective: To assess the feasibility of the fur apposition technique (FAT) for simple laceration closure and to compare this technique to standard suturing in wound healing percentages, repair time, and time to discharge. Methods: This study was a prospective, nonrandomized, controlled feasibility study. Ten client-owned dogs and 2 client-owned cats presenting to an emergency clinic with simple lacerations from June 2022 to September 2023 were assigned to either the standard suturing group or FAT group for simple laceration closure. Lacerations were rechecked at 10 to 14 days, and the wound healing percentage was assessed. Closure repair time and time to discharge were evaluated as secondary outcome measures. Results: All 16 wounds (8 sutured and 8 FAT) were classified as healed by a veterinarian at recheck. No statistical difference was seen when wound healing percentage was evaluated between groups (mean difference, 5.65; t = 0.61; 95% CI, -8.55 to 15.44). The FAT resulted in faster time to discharge (median, 43.0 minutes; IQR, 38.5 to 65.0 minutes) compared to standard suturing (median, 118.0 minutes; IQR, 51.5 to 161.0 minutes; z = -2.32). No difference was found in repair time when the time to suture the wound was evaluated (median, 15.0 minutes; IQR, 6.0 to 25.5 minutes) compared to closure time with FAT (median, 10.0 minutes; IQR, 4.25 to 16.0 minutes; z = -1.053). All clinicians classified FAT as feasible for simple laceration closure in this population. Conclusions: FAT is a feasible, simple laceration closure technique in small animal patients. Clinical Relevance: Methods other than suturing can be considered for simple laceration closure in small animals.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".