The TRAAGIC score: early predictors of inpatient mortality in adult trauma patients
Bibliographic record
Abstract
Background: Scoring systems are important in prognostication and decision-making in the management of trauma patients. However, they often include an extensive list of factors not easily recalled by clinicians on admission. Additionally, multivariable analyses examining predictors of mortality in these patients is lacking. This study aimed to develop and validate a mortality prediction score for adult trauma inpatients. The intention was to create a scoring tool that could be easily remembered and implemented by clinicians. Methods: This is a retrospective analysis of 5175 adult trauma patients treated at a level 1 trauma centre in Hamilton, Ontario, from 2002 to 2013. For derivation of the score, logistic regression was applied to data collected from 2002 to 2006 to identify potential predictors. Variables with p ≤ 0.10 identified from univariable analysis were entered in the multivariable logistic regression. Statistical significance was set at a value of 0.05. The prediction performance of the score was then assessed and validated on data for trauma patients treated from 2007 to 2013. The discrimination ability and calibration of the validation model were assessed. Frequencies, odds ratios with 95% confidence intervals (CIs) and C-statistics were reported. Results: The TRAAGIC prediction score (transfusion, age, airway, hyperglycemia, international normalized ratio, creatinine) showed a C-index of 0.85 (95% CI 0.83–0.87) in the derivation cohort. The TRAAGIC score had high discrimination and good calibration when applied to the validation cohort. Conclusion: The TRAAGIC score is an easily remembered and straightforward toolthat can reasonably predict inpatient mortality for adult trauma patients.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".