Assessment of post-trauma complications in eight million trauma cases over a decade in the USA
Bibliographic record
Abstract
BACKGROUND: Trauma is associated with a significant risk of post-trauma complications (PTCs). These include thromboembolic events, strokes, infections, and failure of organ systems (eg, kidney failure). Although care of the trauma patient has evolved during the last decade, whether this has resulted in a reduction in specific PTCs is unknown. We hypothesize that the incidence of PTCs has been decreasing during a 10-year period from 2007 to 2017. METHODS: This is a descriptive study of trauma patients originating from level 1, 2, 3, and 4 trauma centers in the USA, obtained via the Trauma Quality Improvement Program (TQIP) database from 2007 to 2017. PTCs documented throughout the time frame were extracted along with demographic variables. Multiple regression modeling was used to associate admission year with PTCs, while controlling for age, gender, Glasgow Coma Scale score, and Injury Severity Score. RESULTS: Data from 8 720 026 trauma patients were extracted from the TQIP database. A total of 366 768 patients experienced one or more PTCs. There was a general decrease in the incidence of PTCs during the study period, with the overall incidence dropping from 7.0% in 2007 to 2.8% in 2017. Multiple regression identified a slight decrease in incidence in all PTCs, although deep surgical site infection (SSI), deep venous thrombosis (DVT), and stroke incidences increased when controlled for confounders. DISCUSSION: Overall the incidence of PTCs dropped during the 10-year study period, although deep SSI, DVT, stroke, and cardiac arrest increased during the study period. Better risk prediction tools, enabling a precision medicine approach, are warranted to identify at-risk patients. LEVEL OF EVIDENCE: III.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".