Femoral Fractures in Children Younger Than Three Years
Bibliographic record
Abstract
BACKGROUND: Nonaccidental injury (NAI) in children is a major cause of morbidity and mortality, with fractures being the second most common presentation. The presence of a femur fracture has been reported to be suggestive of nonaccidental trauma in 30% to 60% of young children. The purpose of this study was to determine the percentage of NAI in children younger than 3 years presenting with a femur fracture to a single institution within a western Canadian population. METHODS: A retrospective cohort study was performed for children younger than 3 years who presented to the Alberta Children's Hospital during the years 1994 to 2005. The primary outcome variable was the percentage of NAI associated with femur fracture. Secondary outcome variables included patient demographics, injury characteristics, radiological and other workup, and suspicion of NAI. RESULTS: The overall percentage of NAI was 11% (14/127 patients) and 17% (10/60 patients) in children younger than 12 months. Age younger than 12 months (P = 0.04), nonambulatory status (P = 0.004), delayed presentation (P = 0.002), mechanism of injury unwitnessed or inconsistent (P = 0.008), and other associated injuries (P = 0.006) were significant risk factors for NAI. CONCLUSIONS: Children younger than 3 years who present with femoral fracture are at risk for associated NAI, although perhaps this risk is not as high as previously thought. Regardless, a high index of suspicion is mandatory when these children are encountered, and careful screening with a thorough history, physical examination, and other investigations, where indicated, is warranted to rule out associated NAI. LEVEL OF EVIDENCE: Retrospective cohort study, level IV.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".