Maturation process and international accreditation of trauma system in a rapidly developing country
Bibliographic record
Abstract
BACKGROUND: As trauma systems mature, they are expected to improve patient care, reduce in-hospital complications and optimize outcomes. Qatar has a single trauma center, at the Hamad General Hospital, which serves as the hub for the trauma system that was verified as a level 1 trauma system by the Accreditation Canada International Distinction program in 2014. We hypothesized that this international accreditation was a major step, in the maturation process of the Qatar trauma system, that has positively impacted patient care, reduced complications and improved outcomes of trauma patients in such a rapidly developing country. METHODS: A retrospective analysis of data was conducted for all trauma patients who were admitted between 2010 and 2018. Data were obtained from the level 1 trauma center registry at Hamad Medical Corporation. Patients were divided into Group 1- pre-accreditation (admitted from January 2010 to October 2014) and Group 2- post-accreditation (admitted from November 2014 to December 2018). Patients' characteristics and in-hospital outcomes were analyzed and compared. Data included patients' demographics; injury types, mechanism and injury severity scores, interventions, hospital stay, complications and mortality (pre-hospital and in-hospital). Time series analysis for mortality was performed using expert modeler. RESULTS: Data from a total of 15,864 patients was collected and analyzed. Group 2 patients had more severe injuries in comparison to Group 1 (p<0.05). However, Group 2, had a lower complication rate (ventilator associated pneumonia (VAP)) and a shorter mean hospital length of stay (p<0.05). The overall mortality was 8%. In Group 2; the pre-hospital mortality was higher (52% vs. 41%, p = 0.001), while in-hospital mortality was lower (48% vs. 59%) compared to Group 1 (p = 0.001). CONCLUSIONS: The international recognition and accreditation of the trauma center in 2014 was the key factor in the maturation of the trauma system that improved the in-hospital outcomes. Accreditation also brought other benefits including a reduction in VAP and hospital length of stay. However, further studies are required to explore the maturation process of all individual components of the trauma system including the prehospital setting.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".