The New Injury Severity Score: A More Accurate Predictor of In-Hospital Mortality than the Injury Severity Score
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to determine whether the New Injury Severity Score (NISS) is a better predictor of mortality than the Injury Severity Score (ISS) in general and in subgroups according to age, penetrating trauma, and body region injured. METHODS: The study population consisted of 24,263 patients from three urban Level I trauma centers in the province of Quebec, Canada. Discrimination and calibration of NISS and ISS models were compared using receiver operator characteristic (ROC) curves and Hosmer-Lemeshow statistics. RESULTS: NISS showed better discrimination than ISS (area under the ROC curve = 0.827 vs. 0.819; p = 0.0006) and improved calibration (Hosmer-Leme-show = 62 vs. 112). The advantage of the NISS over the ISS was particularly evident among patients with head/neck injuries (area under the ROC curve = 0.819 vs. 0.784; p < 0.0001; Hosmer-Lemeshow = 59 vs. 350). CONCLUSION: The NISS is a more accurate predictor of in-hospital death than the ISS and should be chosen over the ISS for case-mix control in trauma research, especially in certain subpopulations such as head/neck-injured 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".