Review of Burn Injuries Secondary to Home Oxygen
Notice bibliographique
Résumé
The use of long-term home oxygen therapy (HOT) has become increasingly common for treatment of chronic pulmonary diseases. Although illegal to smoke while on HOT, there is an increasing incidence of burn injuries in those patients who smoke while on HOT. The importance of recognition of the prevalence of this injury, the obstacles faced when treating these patients, and understanding the proposed algorithmic approach to be taken with patients on HOT, including prescription, reassessment, and prevention of burn injury are outlined in this review. Retrospective epidemiological data including circumstances, admission, treatment, and disposition were collected and reviewed on the patients treated from 1999 to 2008 with burns secondary to smoking while on HOT. Seventeen patients sustained injuries secondary to smoking on HOT over the 9-year period; 9 patients were female and 8 were male. All the patients were on HOT for chronic obstructive pulmonary disease. Mean patient age was 69.1 ± 2.5 years and mean TBSA 2.8 ± 0.4%; 11.8% (2/17) sustained inhalation injury requiring intubation and 23.5% (4/17) required wound debridement and skin grafting. Mean hospital stay was 42.8 ± 12.5 days; 10.3 ± 5.4 days in the burn intensive care unit and 32.5 ± 11.0 days in the ward. Before the burn injury, 23.5% (4/17) lived in long-term care facilities. On discharge from hospital, 47.1% (8/17) were transferred to extended care facilities or other acute care hospitals, and 11.8% (2/17) died during their hospitalization. After recovery, there was a 35.3% reduction in patients able to return home and/or live independently. A significant number of burn injuries secondary to smoking while on HOT was observed. These patients differ from standard burn patients because they are older in age, have higher rates of inhalation injury, and have much longer lengths of hospitalization, despite smaller TBSA injuries. Prevention of this injury would improve the safety of the patient and those around them as well as healthcare resource allocation. A proactive multidisciplinary algorithmic approach is presented which can be used to manage patients on HOT at risk for continued smoking to decrease the incidence and the impact of burn injuries in this patient population.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 tête enseignante, 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 ».