Predictors of False-Negative Focused Assessment With Sonography for Trauma Examination in Pediatric Blunt Abdominal Trauma
Bibliographic record
Abstract
OBJECTIVES: This study investigated associations between patient and injury characteristics and false-negative (FN) focused assessment with sonography for trauma (FAST) in pediatric blunt abdominal trauma (BAT). We also evaluated the effects of FN FAST on in-hospital mortality and length of stay (LOS) variables. METHODS: This retrospective cohort studied children younger than 18 years between January 1, 2002, and December 31, 2013, with BAT, documented FAST, and pathologic fluid on computed tomography, surgery, or autopsy. Multivariable and bivariate analyses were used to assess associations between FN FAST and patient injury characteristics, mortality, and hospital LOS. RESULTS: A total of 141 pediatric BAT patients with pathologic free fluid were included. There were no patient or injury characteristics, which conferred increased odds of an FN FAST. Splenic and bladder injury were negatively associated with FN FAST odds ratio of 0.4 (95% confidence interval [CI], 0.2-0.8) and 0.1 (95% CI, 0-0.8). Abbreviated Injury Scale score of 4 or greater to the abdomen and extremity was negatively associated with FN FAST odds ratio of 0.1 (95% CI, 0-0.3) and 0.3 (95% CI, 0.1-0.9). There was no association between FN FAST and mortality. Patients with an FN FAST had increased hospital LOS after controlling for sex, age, and Injury Severity Score. CONCLUSIONS: Clinicians need to be cautious applying a single initial FAST to patients with minor abdominal trauma or with suspected injuries to organs other than the spleen or bladder. Formalized studies to develop risk stratification tools could allow clinicians to integrate FAST into the pediatric patient population in the safest manner possible.
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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 | 0.012 |
| 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 | 0.000 |
| 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".