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Enregistrement W144143408

[Pressure therapy of hypertrophic scar after burns and related research].

2010· article· en· W144143408 sur OpenAlexaboutno aff
Cecilia W. P. Li‐Tsang, Beibei Feng, Kui-Cheng Li

Notice bibliographique

RevuePubMed · 2010
Typearticle
Langueen
Domaine
Thématique
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésHypertrophic scarMedicineUltrasoundSurgeryStage (stratigraphy)UrologyRadiology
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

OBJECTIVE: To investigate the mechanisms of pressure intervention, and to explore the most effective regime for pressure therapy. METHODS: Several trials were carried out to study the efficacy and mechanism of pressure therapy, and the development and application efficacy of a smart pressure monitored suit (SPMS) for scar management. (1) Effectiveness of pressure therapy. Forty-five patients suffered burn on extremities were divided into pressure treatment group (n = 36) and control group (n = 9) according to the random number table. Patients in pressure treatment group were prescribed with a regime of wearing custom pressure garment (10% strain rate of pressure + 9 mm thick local pressure padding) more than 23 hours per day, while no active intervention was conducted on patients in control group. Scar conditions were assessed using the Vancouver Scar Scale (VSS), spectrocolorimeter, and tissue palpation ultrasound system. Data were processed with t test or paired t test. (2) Changes in fibroblasts growth rate under pressure. Fibroblasts extracted from scar tissue excised during surgery were loaded with 0, 1.1, 2.8, 5.6 mm Hg (1 mm Hg = 0.133 kPa) pressure respectively to observe the growth rate of fibroblasts. Data were processed with Fisher LSD post-hoc analysis. (3) Scar thickness upon pressure. The changes in scar thickness upon 0, 5, 15, 25, 35 mm Hg pressure were measured at early stage (1 - 6 months), mid-stage (7 - 12 months), and late stage (more than 12 months) using the high frequency ultrasound imaging system. Data were processed with correlation analysis and regression analysis. (4) Study on application of SPMS. Thirty-six patients with hypertrophic scars once treated with the conventional garment were recruited and they were prescribed with the regime of wearing SPMS for one month. Feedback from all participants in rating conventional garment and SPMS was obtained using self-reported questionnaire. The interface pressure of pressure garment was measured using the Pliance X system. Data were processed with Wilcoxon Sign-Ranks test. RESULTS: (1) Scar thickness, color, and VSS score were significantly improved in pressure treatment group after two-month of pressure intervention. VSS score of the scars in pressure treatment group was lower than that in control group two months after treatment. (2) The growth rate of scar fibroblasts under 5.6 mm Hg pressure was obviously lower than that under 0 mm Hg pressure 2 days after pressure loading (mean deviation = 0.086, P = 0.001). Growth rates of fibroblasts under 2.8 and 5.6 mm Hg pressure were obviously lower than that under 0 mm Hg pressure 3 days after pressure loading (with mean deviation respectively 0.060 and 0.118, P = 0.003, P < 0.001). (3) Scar thickness was significantly reduced upon pressure, and a negative relationship between scar thickness and pressure level was observed (r = -0.96, P < 0.01). (4) The results of SPMS study showed a reduction in both static pressure (19.5%) and dynamic pressure (11.9%) after one month of usage; while there was nearly 50.0% reduction in pressure in conventional garment. SPMS was rated significantly higher than conventional garment in terms of comfort, permeability and clinical efficacy (P ≤ 0.001). CONCLUSIONS: Pressure therapy can effectively inhibit the growth of hypertrophic scar, while its exact mechanism needs further study for verification. SPMS is convenient to apply for patients. It takes less time to fabricate and adjust when compared to the conventional garment. Its clinical effect is positive and it may expand its application to other medical conditions.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Autre · Signal consensuel: aucune
Score de désaccord entre enseignants0,006
Score d'incertitude au seuil0,020

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0010,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0060,001

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.

Tête enseignante Opus0,037
Tête enseignante GPT0,271
Écart entre enseignants0,234 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations11
Publié2010
Routes d'admission1
Résumé présentoui

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