A pilot trauma registry in Peshawar, Pakistan – A roadmap to decreasing the burden of injury – Quality improvement study
Bibliographic record
Abstract
BACKGROUND/LOCAL PROBLEM: In Pakistan, trauma is a significant public health issue accounting for the second leading cause of disability and fifth for healthy years of life lost. Well-developed trauma systems, utilizing trauma registries, have been proven to decrease morbidity and mortality from injuries, and helped to reduce the number of injured patients. In Pakistan, most data on injury are acquired through methods that are retrospective, incomplete, and open to recall bias. To that end, a trauma registry was piloted at the Lady Reading Hospital (LRH) in Peshawar, Pakistan to elucidate the importance of trauma registries in designing healthcare targeted quality improvement initiatives. INTERVENTION: Upon receiving ethics approval, a twenty-five-point registry was piloted at the Lady Reading Hospital. METHODS: The pilot implementation was carried out from May 9th to May 13th, 2018. RESULTS: A total of 267 patients were included in the pilot registry. Motor vehicle collisions were the most prevalent cause of injury (46%). The other causes of injury included falls (24%), blunt assaults (9%), stabs/cuts (8%), gunshots (6%), crush injuries (3%), burns (2%), and blasts/landmines (2%). Most patients were treated in the trauma bay and required no further acute intervention (51%). CONCLUSION: This 5-day pilot trauma registry was the first of its kind in Peshawar, Pakistan, and despite its short course, an immense amount of data was garnered on the epidemiology of injury in the region. Significantly, the data collected can already be used to develop evidence-based changes, which will effectively minimize the impact of trauma.
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".