Efectividad del cianocrilato en la reparación de heridas en cuero cabelludo, región ciliar y zona mentoniana en pediatría
Bibliographic record
Abstract
Introduction. The use of tissue adhesives like cyanoacrylate for wound closure in pediatrics offers significant advantages. It is a simple, fast, and painless procedure, making it an alternative in the pediatric population due to the specific characteristics of these patients. \n \nObjective. To evaluate the effectiveness of tissue adhesive with cyanoacrylate in closing wounds on the scalp or hairy areas and the chin region. \n \nMethodology. A prospective observational study was designed, consisting of pediatric patients who presented to the emergency department with wounds requiring sutures in the chin, eyebrow, and scalp areas. Demographic and clinical variables were assessed, as well as the cooperation of the child and the satisfaction level of parents and healthcare professionals after the application of cyanoacrylate. Scar healing was assessed at 3 months. Data were analyzed using the statistical software SPSS. \n \nResults. Wounds in hairy areas showed proper epithelialization with an index ≤2 according to the Vancouver scale. Statistically significant differences were found in the relationship between wound location and its healing (p<0.05). Additionally, a moderate level of agreement was observed between parent and professional satisfaction, with a p-value <0.001. \n \nDiscusión. Cyanoacrylate-based tissue adhesives appear to be a valid and safe alternative for wound repair in areas with hair follicles. They are well-received by patients, their families, and the professionals who performed the procedure. The results regarding scar healing evaluated at 3 months are more satisfactory on the scalp and eyebrow compared to the chin region.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".