Venous Thromboembolism after Severe Injury in Children
Bibliographic record
Abstract
BACKGROUND: Deep vein thrombosis and pulmonary embolism are considered common complications after major trauma. Their incidence and the associated risk factors have rarely been identified in injured children. METHODS: Severely injured children (age <18 years; admitted in a pediatric intensive care unit or length of stay > or = 72 h) with a discharge diagnosis of venous thromboembolism (VTE; deep venous thrombosis and/or pulmonary embolism) were identified from the institutional trauma registry between January 1, 1999 and April 31, 2002. The study centers included a dedicated pediatric trauma center and an adult trauma center with pediatric patients. Risk factors for VTE were identified using multivariate analysis. RESULTS: VTE was found in 11 of the 3,291 admissions, for a rate of 3.3/1,000 admissions. Children with VTE were older and had higher Injury Severity Scores. Independent risk factors for VTE included thoracic injuries [odds ratio (OR): 6.9; 95% confidence interval (CI): 1.4-35.1] and spinal injuries (OR: 37.4; 95% CI: 3.5-396.7). The greatest risk of VTE was in children with central venous catheters (OR: 64.0; 95% CI: 16.8-243.9). CONCLUSION: Older children with high Injury Severity Scores, thoracic injuries, spinal injuries or venous catheters are at risk for VTE. Because VTE prophylaxis, screening and treatment are associated with complications and costs, it is essential to identify subgroups of pediatric patients in whom these strategies might be studied.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".