Implementation of a hospital-based trauma registry in India
Notice bibliographique
Résumé
Background: The injury is responsible for a significantly high burden of disease globally, particularly in the low and middle income countries (LMICs). The epidemiological data of injury can help to identify risk factors for injury and target interventions to improve quality of care. Trauma registries (TR) have been recognized as an essential tool in decreasing death and disability rates from injuries. The importance of trauma registry has been widely recognized in the developing countries, but it is still underutilized due to lack of awareness, resources, and funding. The objective of the study was to explore the feasibility of the trauma registry by implementing it at a tertiary care hospital and estimate the epidemiology of the injury. Method: The study was conducted at the casualty of the Surat Municipal Institute of Medical Education and Research (SMIMER) hospital, Surat, India during June 2018 to August 2018. Data were collected on the paper form of TR after taking consent from the patients presented to the casualty department with the sustained injury. TR was developed at the center of the global surgery, McGill University Health Centre, Montreal, Canada. Data about patient demographics, causal event, injury-related physiologic, anatomic data, and clinical outcomes were recorded. Data were entered in the electronic version of the TR and analysis was done. Result: A total of 716 patients were included in the study. The mean age of the patient was 33 year, and 74.16% were male with maximum patients were in the age group of 20-25 and 30-35. Motor vehicle collision (34.64%) and Fall (29.89 %) were the most common causes of the injury followed by blunt trauma (13.41%). Students (28%) and unemployed (17%) were most frequently affected with majority of patients having primary and secondary education. 39.25 % were brought by the ambulance whereas 30.31% of patients arrived by private vehicle and 22.35% came by public transport. Cut/Open wound (46%) accounted for the majority of the injury followed by thoracic injury (22%) and head injury (19%). According to Kampala Trauma Score (KTS) calculation, 1.4% were severely injured compared to 91.8% mildly injured. Twenty patients died in the hospital, mainly injured due to fall and Motor Vehicle Collision. Conclusion:Trauma registry was effective to capture injury-related information in a simple one-page proforma in the study which would be helpful to assess the trauma burden and evaluate the effectiveness of care given to the patients. The continuous use of the TR is imperative to ensure high quality data and adequate population coverage and a collaborative effort is needed in India for successful implementation.
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 | 0,007 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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.
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 ».