Implementation of a hospital-based trauma registry in India
Bibliographic record
Abstract
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.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".