Clinical features and outcomes of blunt splenic injury in children
Bibliographic record
Abstract
Although the spleen is the most commonly injured intra-abdominal organ after blunt trauma, there are limited data available in China. The objectives of this study were to investigate the clinical features and determine the risk factors for operative management (OM) in children with blunt splenic injury (BSI).A review of the medical records of children diagnosed with BSI between January 2010 and September 2016 at West China Hospital of Sichuan University was performed.A total of 101 patients diagnosed with BSI were recruited, including 76 patients transferred from other hospitals. The male-to-female ratio was 2.06:1, with a mean age of 7.8 years old. The most common injury season was summer and the most common injury mechanism was road traffic accidents. Sixty-eight patients suffered multiple injuries. Thirty-four patients received blood transfusions. Two patients died from multiple organ failure or hemorrhagic shock. Significant differences were observed in the injury season, injury mechanism, injury date, and hemoglobin levels between the isolated injury group and the multiple injuries group. The overall operative rate was 29.7%. Multivariate regression analysis revealed that age, blood transfusion, and grade of injury were independent risk factors for OM.Our study provided evidence that the management of pediatric BSI was variable. The operative rate in pediatric BSI may be higher in certain patient groups. Although nonoperative management is one of the standard treatment options, our data suggest that OM is an appropriate way to treat patients who are hemodynamically unstable.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".