Research trends in pediatric splenic trauma in Brazil: how much has changed in the last two decades?
Bibliographic record
Abstract
PURPOSE: Research in high-income countries has extensively documented the non-operative management of spleen injuries in children, resulting in low splenectomy rates (5%). However, there is a lack of literature on this topic in low- and-middle-income countries (LMICs), including Brazil. This scoping review analyzed pediatric spleen trauma research trends in Brazil and the United States of America (USA). METHODS: Search strategy was conducted across five databases, considering articles published in English or Portuguese from January 1968 to 2023 that reported spleen injury in patients younger than 18 years old in Brazil or the USA. Two pairs of independent reviewers screened the title and the abstract, followed by a full-text review. RESULTS: The total of 7,150 studies was identified, of which 295 were eligible for data extraction. Most papers (98.64%, 301) originated from the USA, while only 1.36% (4) were from Brazil. In addition, 46.44% (137) articles reported intrabdominal injury, including splenic trauma, 16.27% (48) liver and spleen injury, and 37.29% (110) reported isolated spleen injury. The operative rate for spleen injury was 11.33% in American studies (40,812/359,926) compared to 98.57% (137/139) in Brazilian studies. CONCLUSIONS: Brazil contributed only with four studies on pediatric splenic trauma over two decades. Future studies should explore the incidence and management of splenic trauma in LMICs.
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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 | 0.022 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.014 | 0.026 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".