THE PREVALENCE OF NON-ACCIDENTAL INJURY IN TODDLERS WITH FEMORAL FRACTURES - THE CALGARY EXPERIENCE
Bibliographic record
Abstract
The purpose of this study was to determine the prevalence of non-accidental injury in toddlers (birth to two years old) presenting with a femur fracture to a single institution within a Western Canadian population. There are currently no Canadian studies that investigate this association in the medical literature. A retrospective case-control study was performed for children less than or equal to two years of age with a diagnosis of femur fracture who presented to a single institution from 1994 to 2005. Patients with associated pathological or metabolic bone disorders were excluded. The primary outcome measure was the prevalence of non-accidental injury associated with femur fracture. Secondary outcome measures included: patient demographics, injury characteristics, radiologic and other work-up, and suspicion of abuse. Statistical analysis was performed using tests of analysis of variance and multivariate analysis to identify independent and significant risk factors associated with non-accidental injury. One hundred twenty-seven children that met the inclusion criteria were identified. The overall prevalence of non-accidental injury was 11% (14/127 patients) and 17% (10/60 patients) in children under twelve months. Mean age was twelve months in the abused group and sixteen months in the accidental group. Forty-three percent of the abused group had delayed presentation compared to 8% of the accidental group. In the abused group, 43% had unknown, unwitnessed, inconsistent histories versus 4% in the accidental group. No specific fracture pattern or location suggestive for non-accidental injury was identified. Forty-three percent (6/14 patients) of the abused group and 12% (13/113 patients) of the accidental group had other injuries. Multivariate analysis determined that delayed presentation (p=0.001), mechanism of injury (p=0.02) and other associated injuries (p=0.003) were the only independently significant risk factors for abuse. Toddlers who present with femoral fracture are at risk for associated non-accidental injury though 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, are warranted to rule out associated child abuse.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".