Mechanism of Injury Affects 6-Month Functional Outcome in Children Hospitalized Because of Severe Injuries
Bibliographic record
Abstract
BACKGROUND: The burden of childhood injury is often described using vital statistics for mortality and hospital admissions as a measure of morbidity. Hospital admissions, however, reflect the process of care and do not directly measure children's functional disability. The purpose of this study was to determine the influence of mechanism of injury on the functional outcome 6 months after injury in children in an inpatient trauma unit of a pediatric referral hospital. METHODS: A retrospective cohort of 357 children aged 2 to 15 with an Injury Severity Score (ISS) > 12 was studied to determine the relationship between mechanism of injury (based on International Classification of Diseases, Ninth Revision e-code) and functional outcome 6 months after hospital discharge. Wee Functional Independence Measure (WeeFIM) was used to assess functional outcome. Any child with a WeeFIM score less than the maximum (of 126) attainable was classed as requiring assistance, and the relative risk of requiring assistance at 6 months was calculated for each injury mechanism. Poisson regression analysis was used to assess the importance of mechanism of injury, after adjusting for age, gender, ISS, and a primary diagnosis of central nervous system (CNS) injury. RESULTS: Mechanism of injury had a significant effect on the functional outcome at 6 months: 72% of pedestrians, 64% of cyclists struck by cars, and 59% of injured motor vehicle occupants required assistance during daily activities. By contrast, only 27% of those injured playing sports and 22% of cyclists injured without motor vehicle involvement required assistance. The relative risk of children requiring assistance was similar with or without adjustment for age, gender, ISS, and CNS injury. CONCLUSION: Mechanism of injury is significantly associated with requiring assistance 6 months postdischarge, even after controlling for age, injury severity, and the presence of a CNS injury. These data are important both when discussing the prognosis for an individual patient and also when considering the population impact of childhood injuries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".