104 Complications following chin laceration reparation using tissue adhesive compared to suture in children
Bibliographic record
Abstract
Tissue adhesive is widely used in the emergency department to repair minor lacerations but there exists a debate as to whether it should be used for chin lacerations. To evaluate the proportion of wound dehiscence of chin lacerations repaired with tissue adhesive in comparison to sutures. This was a retrospective cohort study including all children requiring a facial laceration reparation in a single tertiary care pediatric hospital during a two-year period. The primary outcome was wound dehiscence in the 30 days following reparation. The independent variable of interest was the use of tissue adhesive vs suture. Other variables included size and localization of the laceration. The primary analysis was the association between method of reparation and risk of dehiscence for chin laceration. Other analysis compared risk of dehiscence according to the localization. A random sample of charts was reviewed in duplicate to insure reliability of the chart review and only variables with a good reliability were included in the analysis. Among the 2,044 children presenting with a facial laceration requiring an intervention, 1,804 (88%) were repaired using tissue adhesive. The laceration was located on the chin in 360 (18%) of patients. The use of tissue adhesive was not statistically associated with a higher risk of dehiscence for all facial lacerations (difference: 0.2; 95%CI: -1.9 to 0.8%), nor for chin lacerations (difference 2.2%; 95%CI: -7.5 to 4.4%). However, the probability of dehiscence was higher for chin laceration in comparison to other localizations (difference of 1.6%; 95%CI: 0.5–3.6%). While the proportion of dehiscence was higher for chin lacerations compared to other facial localizations, the risk of dehiscence was not statistically different for chin laceration repaired with tissue adhesive or sutures.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".