572 Exploring Frailty in Rural Burn Patient Outcomes
Notice bibliographique
Résumé
Abstract Introduction The Department of Agriculture estimates that 1 in 7 Americans live in rural areas. When patients from these regions sustain burn injuries, previous research shows they tend to experience larger burns and higher mortality rates compared to their urban counterparts. This demographic is often older, and experiences more chronic health conditions, factors often associated with frailty. Despite these findings, there is limited research on how frailty impacts burn outcomes among rural populations. This study aims to fill that gap by analyzing the effect of frailty on burn patient outcomes across these different communities. Methods Following IRB approval, a retrospective chart review was conducted for burn patients over 50 admitted to a burn center between January 2021 and December 2022. Data collected included burn injury details, substance use, and patients’ reported zip codes. Rural-Urban Commuting Area (RUCA) codes determined by zip code were used to determine if patients lived in rural or urban areas. Frailty scores were calculated using the Canadian Study of Health and Aging Clinical Frailty Scale (CSHA CFS). Statistical analysis was conducted using SAS software (version 9.4) to perform Chi-square, Fisher Exact, and Wilcoxon 2-sample tests. Results are presented as median (interquartile range). Results The study analyzed 451 patients with a median age of 62 (IQR 14). Of these, 305 (67.6%) were male, and the median burn size was 5% (IQR 11). A total of 46 (10.2%) died from their injuries. Among the participants, 110 patients (24.4%) resided in rural areas, and the median frailty score was 4 (IQR 2). Rural patients were more likely to be White (20.9% vs. 15.5%, p=0.005). No statistically significant differences were found between rural and urban patients in terms of age (63.5 years [IQR 16] vs. 62 [IQR 13], p=0.18), burn size (6% [IQR 12] vs. 5% [IQR 11], p=0.17), frailty (4 [IQR 1] vs. 4 [IQR 2], p=0.15), or mortality (9.1% vs. 10.6%, p=0.66). There were also no significant differences in length of hospital stay (13 days [IQR 19] vs. 10 days [IQR 18], p=0.37). There were also no differences in positive toxicology screen results (25.5% vs. 24.3%, p=0.19), methamphetamine positivity (24.6% vs. 23.3%, p=0.79), or alcohol use (6.4% vs. 6.1%, p=0.39) between rural and urban patients. Conclusions This study found no significant burn characteristics or outcomes differences between rural and urban patients. Moreover, rural patients had similar rates of substance use compared to their urban counterparts. Based on these findings, healthcare providers should avoid making assumptions about a patient’s substance use or outcomes based solely on whether they come from a rural or urban area. Applicability of Research to Practice Burn injury outcomes for patients from rural areas may be comparable to those from urban areas. This challenges previous assumptions and highlights the need for further research to address the specific needs of burn survivors, regardless of community type. Funding for the Study N/A
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 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 | 0,002 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| 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 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».