Bibliographic record
Abstract
OBJECTIVES: Imaging algorithms for assessment of thoracic trauma in pediatric patients remain controversial, attempting to balance radiation dose and its associated risk with the need for thorough assessment of patients' injuries. This study reviewed the value of chest radiography in detecting traumatic injuries, and the impact that computed tomography (CT) had on clinical management. METHODS: A retrospective review of pediatric trauma patients undergoing chest radiography and thoracic CT over a 2-year period at a level 1 trauma center was performed. The incidence of various traumatic injuries was documented, with measures of sensitivity and specificity on radiography. Clinical notes were reviewed to identify any changes in care based on CT findings. RESULTS: Eighty-one pediatric trauma patients underwent thoracic CT over a 2-year period, with 60 patients meeting the inclusion criteria. Radiographs identified 47 traumatic injuries out of 117 seen on the subsequent CT examinations for a sensitivity of 41% and specificity of 91%. Radiographs were most sensitive in detecting osseous injuries with a sensitivity of 54%. Additional CT findings changed management in 2 of 60 cases, or 3.3% of the time. CONCLUSIONS: Use of thoracic CT in pediatric trauma patients identifies a significantly greater number of injuries compared with than radiography but significantly increases radiation dose while changing management in only a very small proportion of cases. Despite the relatively small sample size, the findings reflect 2 years of experience at a level 1 trauma center, and this study suggests that it may be reasonable to decrease the frequency of cross-sectional imaging.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".