Occurrence Patterns of Traumatic Brain Injury Within the Emergency Department and Internal Screening Process Efficacy During the COVID-19 Pandemic: Retrospective Analysis
Notice bibliographique
Résumé
BACKGROUND: Traumatic brain injury (TBI) is one of the leading causes of death in pediatric patients. Continued recruitment of pediatric TBI participants into a biobank amidst the COVID-19 pandemic not only necessitates adaptive changes to traditional recruitment methods but also requires an evaluation of emergency department (ED) utilization by TBI-presenting patients. OBJECTIVE: The primary objective of this exploratory retrospective study was to evaluate pediatric TBI-related ED utilization during the pandemic. The secondary objective was to appraise the efficacy of the research team's internal screening processes. METHODS: Potential participants (ie, individuals who met all inclusion criteria and would be approached by a consenter) were screened from an ED's electronic health record system. Data regarding their visit were recorded in a Health Insurance Portability and Accountability Act-compliant manner, which were cleaned through Google Sheets. Cleaned data were then coded as either a screening variable or a hospital utilization variable to examine the effects of the pandemic on internal operations and hospital utilization patterns. The variables were compared between select months during the pandemic in 2020 to analogous months in 2019 in the R programming language via the two-sample Student t test and the Mann-Whitney-Wilcoxon rank-sum test. RESULTS: The sample (N=2321) consisted of 1245 entries from 2019 and 1076 entries from 2020. A significantly greater proportion of potential participants (P<.001) were identified in 2020 (222/633, 35.1%) than in 2019 (195/908, 21.4%). A significantly greater proportion of potential participants (P<.001) had a visit reason indicative of a TBI in 2020 (181/222, 81.5%) than in 2019 (103/195, 52.8%). A significantly greater proportion of these injuries (P=.02) occurred inside (39/181, 21.5%) in 2020 than in 2019 (11/103, 10.7%). No significant difference was found across the mechanism of injury categories reported for potential participants between 2019 and 2020. Potential participants were significantly older (P=.006) in 2019 (mean 8.93 years) than in 2020 (mean 7.31 years). Screeners spent significantly longer (P=.03) to identify potential participants in March 2020 (55 minutes) than in March 2019 (32 minutes), but spent significantly less time (P=.01) to do so in July 2020 (22 minutes) than in July 2019 (42 minutes). Screening coverage was significantly lower (P<.001) in March 2020 (241.8 hours) than in March 2019 (346.5 hours). Screening coverage was significantly greater (P<.001) in April 2020 (611.5 hours) and July 2020 (513.5 hours) than in April 2019 (470.5 hours) and July 2019 (404.3 hours), respectively. CONCLUSIONS: There was a significant increase in the rate of incoming TBI cases to the ED during the COVID-19 pandemic, warranting continued enrollment with added safety measures. Additionally, refinement of internal processes improved the accuracy of data collection. As demonstrated in this study, researchers can leverage ongoing data collection to facilitate process improvements and evaluate the impact of unexpected global events on their research.
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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 | 0,011 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».