Predicting Thoracic Injury in Children With Multitrauma
Bibliographic record
Abstract
OBJECTIVES: Previous pediatric trauma studies focused on predictors of abnormal chest radiographs or included patients with low injury severity. This study identified predictors of thoracic injury (TI) diagnoses in a high-risk population and determined TI rate without predictors. METHODS: This study was a retrospective trauma registry analysis of previously healthy children aged 0 to 17 years with multisystem blunt trauma requiring trauma team activation and chest radiography who were divided into those with and without TI. Plausible TI predictors included Glasgow Coma Scale score of 13 or less, abnormal thoracic symptoms/signs, abnormal chest auscultation, respiratory distress/ rate higher than the 95th percentile, oxygen saturation less than 95%, abnormal abdominal signs/symptoms, tachycardia higher than the 95th percentile, blood pressure lower than the 5th percentile, and femur fracture. RESULTS: One hundred forty-one (29%) of 493 eligible patients had TI. Independent TI predictors include thoracic symptoms/signs (odds ratio [OR], 6.0; 95% confidence interval [CI], 3.6-10.1), abnormal chest auscultation (OR, 3.5; 95% CI, 2.0-6.2), saturation less than 95% (OR, 3.1; 95% CI, 1.8-5.5), blood pressure lower than the 5th percentile (OR, 3.7; 95% CI, 1.1-12.2), and femur fracture (OR, 2.5; 95% CI, 1.2-5.4). Six (5%) of 119 children (95% CI, 0.01-0.09) without predictors had TI. CONCLUSIONS: Predictors of TI include thoracic symptoms/signs, abnormal chest auscultation, saturation less than 95%, blood pressure lower than the 5th percentile, and femur fracture. Because an important portion of children without predictors had TI, chest radiography should remain part of pediatric trauma resuscitation.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".