Bibliographic record
Abstract
Background: Traumatic injury accounts for 7.8% of all deaths globally, and 30% to 40% of those deaths are due to hemorrhage. Shock Index (SI) has been found to be useful in the recognition of hemorrhage but no definite threshold for predicting mortality has been determined. Our aim was to determine whether a SI ≥ 1 in adult trauma patients was associated with increased in-hospital mortality compared to a SI < 1. Methods: We conducted a systematic review and meta-analysis using Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. EMBASE, MEDLINE, and Cochrane Library were searched using controlled vocabulary, and retrospective observational studies were included. Studies were included if they reported in-hospital mortality in trauma patients aged ≥ 16 years, with a measurement of SI from the emergency department or trauma center, dividing patients in groups of SI ≥ 1 and SI < 1. Risk of bias was assessed by using the Newcastle-Ottawa Scale, and the strength and quality of the body of evidence was assessed according to GRADE. Data was pooled using a random effects model.Results: We screened 1239 citations with an inter-rater reliability (Cohen’s kappa) of 0.90 (95% CI 0.88-0.93). Thirteen comparative cohort studies including 639210 patients were included. All studies reported a significant higher in-hospital mortality in adult trauma patients with a SI ≥ 1 compared to those having a SI < 1 at first assessment in the emergency department or trauma center. Eleven studies were included in the meta-analysis. The pooled risk ratio (RR) of in-hospital mortality was RR 4.29 (95% confidence interval 3.00 - 6.12). The overall quality of evidence was low. Conclusion: This systematic review found a fourfold risk of in-hospital mortality in adult trauma patients with an initial SI ≥ 1 in the emergency department or trauma center.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.011 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".