Statistical Validation of the Revised Trauma Score
Bibliographic record
Abstract
BACKGROUND: To validate the accuracy of the Revised Trauma Score (RTS) and its components for predicting in-hospital mortality. METHODS: Analyses were based on 22,388 patients from the trauma registries of three urban Level I trauma centers in the province of Quebec, Canada. The accuracy of RTS coded variables for the Glasgow Coma Score (GCSc), Systolic Blood Pressure (SBPc), and Respiratory Rate (RRc) for predicting mortality was evaluated in logistic regression models with measures of discrimination and model fit and compared with Fractional Polynomial (FP) transformations of each component. RESULTS: RTS coded variables were associated with sparse data distributions and did not accurately represent the relation of GCS, SBP, and RR to mortality. FP models were always associated with significantly better discrimination (all p < 0.00001) and model fit. Survival probability estimates generated by the model with FP transformations were significantly different to those generated by the model with RTS-coded variables. CONCLUSIONS: The RTS in its present form does not accurately describe the relation of GCS, SBP, and RR to mortality. FP transformation would improve the accuracy of predicted survival probabilities used for performance evaluation and may improve control of confounding caused by of physiologic severity case mix in trauma research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.050 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".