Prevalence of Abuse Among Young Children With Rib Fractures
Bibliographic record
Abstract
OBJECTIVES: We aimed to estimate the prevalence of abuse in young children presenting with rib fractures and to identify demographic, injury, and presentation-related characteristics that affect the probability that rib fractures are secondary to abuse. METHODS: We searched PubMed/MEDLINE and CINAHL databases for articles published in English between January 1, 1990, and June 30, 2014 on rib fracture etiology in children 5 years or younger. Two reviewers independently extracted predefined data elements and assigned quality ratings to included studies. Study-specific abuse prevalences and the sensitivities, specificities, and positive and negative likelihood ratios of patients' demographic and clinical characteristics for abuse were calculated with 95% confidence intervals. RESULTS: Data for 1396 children 48 months or younger with rib fractures were abstracted from 10 articles. Among infants younger than 12 months, abuse prevalence ranged from 67% to 82%, whereas children 12 to 23 and 24 to 35 months old had study-specific abuse prevalences of 29% and 28%, respectively. Age younger than 12 months was the only characteristic significantly associated with increased likelihood of abuse across multiple studies. Rib fracture location was not associated with likelihood of abuse. The retrospective design of the included studies and variations in ascertainment of cases, inclusion/exclusion criteria, and child abuse assessments prevented further meta-analysis. CONCLUSIONS: Abuse is the most common cause of rib fractures in infants younger than 12 months. Prospective studies with standardized methods are needed to improve accuracy in determining abuse prevalence among children with rib fractures and characteristics associated with abusive rib fractures.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".