The Epidemiology of Nonaccidental Trauma in Children
Bibliographic record
Abstract
BACKGROUND: Abuse of children is abhorrent in Western society and, yet, is not uncommon. Nonaccidental trauma (NAT) is the result of a complex sociopathology. Not all of the causative factors of NAT are known, many are incompletely described, not all function in each case, and many are secondary to preexisting pathology in other areas. QUESTIONS/PURPOSES: We therefore addressed the following questions in this review: (1) what is the general incidence of NAT; (2) what factors are intrinsic to the abused child, family, and society; and (3) what orthopaedic injuries are common in NAT? METHODS: We searched Medline, Medline In Process & Other Non-Indexed Citations, and Embase using OVID. Only one article fit our inclusion criteria; therefore, this is a descriptive generalized review of the epidemiology of NAT. RESULTS: The general incidence of NAT ranges from 0.47 per 100,000 to 2000 per 100,000. Younger children are at greater risk of NAT than older children. Parents are often the perpetrators of the abuse. Rib fractures are highly indicative of NAT in young children. CONCLUSIONS: It is important to consider child, family, and societal factors when confronted with suspicions of child abuse. Our review demonstrates the currently limited information on the true incidence of NAT. To determine a much more accurate incidence of NAT, there needs to be a population-based surveillance program conducted through primary care providers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".