Fracture patterns in suspected non-accidental injury across neurodevelopmental milestones: A systematic review and evidence-informed plausibility framework
Bibliographic record
Abstract
BACKGROUND: Determining whether a pediatric fracture is congruent with the reported mechanism is challenging even with age-based guidance. We synthesize fracture evidence by neurodevelopmental milestones to organize plausibility judgments that augment, not replace, multidisciplinary abuse evaluations. OBJECTIVE: To synthesize evidence linking fracture patterns to developmental milestones in children ≤36 months evaluated for suspected non-accidental injury (NAI) and to propose a milestone-anchored, evidence-informed plausibility framework. METHODS: Following PRISMA 2020, we searched PubMed, Embase and Scopus from inception to 19 August 2025. Two reviewers independently screened/extracted data and assessed risk of bias (Newcastle-Ottawa Scale for cohort/case-control). PROTOCOL REGISTRATION: PROSPERO CRD420251120765. Eligibility required data linking fracture type/location to age or explicit motor milestones (e.g., pre-rolling, crawling and walking). Quantitative synthesis was restricted to within-cohort strata: we calculated stratum-specific odds ratios, a Mantel-Haenszel within-cohort stratified pooled estimate across long-bone strata and (where informative) Bayesian Beta-Binomial posterior summaries; cross-cohort pooling was avoided because of cohort overlap and heterogeneous abuse determination. RESULTS: Across primary studies, non-ambulatory infants with long-bone (diaphyseal) fractures had markedly higher odds of abuse than ambulatory toddlers (within-cohort pooled OR 15.12, 95 % CI 2.88-79.36). Infants <12 months with rib fractures had abuse prevalences 67-82 % when motor-vehicle crashes and bone disease were included and ~91 % when excluded; rib fracture location was not independently associated with abuse likelihood. Classic metaphyseal lesions (CMLs) are highly suggestive, though not pathognomonic, of abuse in non-ambulatory infants. In one cohort, multiple fractures were present in 84 % (16/19) of children reported as suspected abuse. Collectively, findings support a milestone-aware approach to plausibility. CONCLUSIONS: Fracture patterns correlate with developmental capabilities. Organized by milestones, these data inform, but do not determine, abuse evaluations and should be integrated with history, examination, guideline-concordant imaging, differential for bone fragility when indicated and multidisciplinary assessment.
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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 | 0.073 | 0.310 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.020 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".