A Comparison of the Kampala Trauma Score with the Revised Trauma Score in a Cohort of Colombian Trauma Patients
Bibliographic record
Abstract
ABSTRACT Introduction To date, no trauma scoring system has emerged as the gold standard for use in developing countries, where limited resources for data collection are a major issue. The purpose of this study is to compare the relatively recently developed and simply calculated KTS (Kampala Trauma Score) with the more widely used RTS (Revised Trauma Score) within a cohort of Colombian trauma patients. Materials and methods Data on over 2,200 patients was derived from a newly developed trauma registry in Colombia. A statistical analysis was done using SPSS software, and included simple linear and logistical regression as appropriate. Results Both the KTS and RTS were statistically significant in terms of their ability to predict death and length of stay in hospital with the KTS being a better predictor of both. The simplest model predicting death used only the neurologic component of the KTS. However, none of these three scores explained a very large amount of the variation in the dataset. Conclusion Although statistically significant, neither the KTS nor the RTS performed well at predicting death or length of hospital stay. However, the simpler KTS did perform somewhat better than the slightly more complex RTS. Using the extremely simple neurologic component of the KTS on its own proved to be the best predictor of length of hospital stay, and also outperformed the RTS in regards to death prediction. It is clear from this study that the optimal injury scoring system for use in under resourced environments remains allusive with further research warranted. How to cite this article Clarkson CA, Clarkson C, Rubiano AM, Borgaonkar M. A Comparison of the Kampala Trauma Score with the Revised Trauma Score in a Cohort of Colombian Trauma Patients. Panam J Trauma Critical Care Emerg Surg 2012;1(3):146-149.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".