Observational studyBMC emergency medicine2025
The effect of a new in-hospital trauma care model on the outcomes of severely injured trauma patients in the emergency department: a retrospective observational study in China.
Observational study in BMC emergency medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Application of key link nursing based on feedforward control theory in the emergency rescue of multiple trauma patients.Frontiers in public health · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
backgroundThe mortality and disability rates among severely injured trauma patients are very high. This study aimed to investigate whether a new in-hospital trauma care model can improve emergency care efficiency and enhance the prognosis of severely injured trauma patients.
methodsThis retrospective observational study included 366 severely injured trauma patients (ISS ≥ 16) who were admitted to the emergency department of a tertiary hospital between 2023 and 2024. Based on the emergency care model used, patients were divided into the traditional model group (n = 213) from January to April 2023 and the new model group (n = 153) from January to April 2024. The general clinical data, prognosis information, as well as seven emergency quality control indicators for both groups were collected and analyzed.
resultsThe study included 270 male patients (73.8%) and 96 female patients (26.2%), with a mean age of 56 (44, 69) years. No significant differences were found between the two groups regarding gender, age, time since injury, mechanism of injury, and vital signs upon admission (P > 0.05). The new model group had significantly shorter times for establishing effective circulation access (15.66 ± 3.36 vs. 9.44 ± 3.18 min), establishing an artificial airway (36.90 ± 12.23 vs. 23.91 ± 9.07 min), preparing blood transfusion (48.84 ± 5.73 vs. 31.0 ± 64.67 min), completing whole-body CT scans (57.18 ± 8.26 vs. 42.17 ± 7.28 min), and developing a definitive treatment plan (77.45 ± 6.26 vs. 56.50 ± 6.35 min) compared to the traditional model group. Additionally, the new model group had a significantly higher rate of bedside FAST completion (92.8% vs. 53.1%) and a higher success rate of resuscitation within the first hour (70.9% vs. 85.0%) than the traditional model group. Regarding prognosis, the new model group had a lower overall in-hospital mortality rate (12.1% vs. 5.9%) and a lower incidence of complications such as DIC and ARDS (23.9% vs. 9.2%, all P < 0.05).
conclusionThe new in-hospital trauma care model significantly enhanced the in-hospital emergency care efficiency, reduced in-hospital mortality, and decreased the incidence of complications for severely injured patients, which may serve as a useful reference for developing countries in similar settings. CLINICAL TRIAL NUMBER: Not applicable.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.