A customized postoperative wound management model tailored to the healing dynamics of the upper lip: a retrospective cohort study
Notice bibliographique
Résumé
BACKGROUND: Clinical observations suggest that upper lip injuries often exhibit prolonged healing and reduced healing efficacy. The unique anatomical and physiological characteristics of the lips make them particularly susceptible to infection and delayed recovery, posing significant challenges for postoperative management. The aim of this research, grounded in the anatomical and physiological subtleties of the upper lip, was to contrast the wound management outcomes between patients treated with a conventional wound management approach and those treated with a customized wound management model. Additionally, it aimed to explore the efficacy of a dynamic model in the assessment and treatment of upper lip wounds. METHOD: It is a retrospective cohort study. Patients with maxillofacial injuries who attended the Emergency Department of West China Hospital of Stomatology of Sichuan University were selected for retrospective analysis. Between February and August 2023, 89 out of 783 patients with maxillofacial injuries who had upper lip injuries formed the control group. From February to August 2024, 84 patients with upper lip injuries, selected from 643 patients with maxillofacial injuries, constituted the experimental group. The control group applied the traditional wound management method, while the experimental group implemented the customized wound management model, which involved optimizing the team, devising a wound-healing model, improving dressings, innovating in both the timing and method of dressing changes, and providing customized care for complex wounds. The outcomes of upper lip wound management between the two groups were compared, including the wound infection rate, the Vancouver Scar Scale (VSS) score 30 days after surgery, and patients' satisfaction. Data analysis was conducted using SPSS 23.0 software. RESULTS: There were no statistically significant differences in the demographic variables between the two groups (P > 0.05). The upper lip infection rates were 18% (16 out of 89) in the control group and 1.2% (1 out of 84) in the experimental group, respectively. Notably, the incidence of wound complications and wound infection rates differed significantly between the control and experimental groups, with the experimental group showing lower figures for both (P < 0.001). Compared to the control group, the experimental group had a lower VSS score and a higher patients' satisfaction score, with these differences being statistically significant (P < 0.001). CONCLUSION: The customized wound management model demonstrated superior clinical efficacy in treating upper lip injuries. Patients gain advantages from a customized postoperative wound management model designed according to the upper lip healing kinetics. By aligning postoperative care with the unique healing kinetics of the upper lip, this approach significantly reduces infection rates and enhances patient satisfaction. These findings advocate for wider adoption of this personalized wound management model in the clinical treatment of upper lip injuries. TRIAL REGISTRATION: Clinical Trial Registration No. ChiCTR2300079287, Registration Date 29 December 2023.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».