Risk of Maltreatment-Related Injury: A Cross-Sectional Study of Children under Five Years Old Admitted to Hospital with a Head or Neck Injury or Fracture
Bibliographic record
Abstract
OBJECTIVES: To determine the predictive value and sensitivity of demographic features and injuries (indicators) for maltreatment-related codes in hospital discharge records of children admitted with a head or neck injury or fracture. STUDY DESIGN: Population-based, cross sectional study. SETTING: NHS hospitals in England. SUBJECTS: Children under five years old admitted acutely to hospital with head or neck injury or fracture. DATA SOURCE: Hospital Episodes Statistics, 1997 to 2009. MAIN OUTCOME MEASURE: Maltreatment-related injury admissions, defined by ICD10 codes, were used to calculate for each indicator (demographic feature and/or type of injury): i) the predictive value (proportion of injury admissions that were maltreatment-related); ii) sensitivity (proportion of all maltreatment-related injury admissions with the indicator). RESULTS: Of 260,294 childhood admissions for fracture or head or neck injury, 3.2% (8,337) were maltreatment-related. With increasing age of the child, the predictive value for maltreatment-related injury declined but sensitivity increased. Half of the maltreatment-related admissions occurred in children older than one year, and 63% occurred in children with head injuries without fractures or intracranial injury. CONCLUSIONS: Highly predictive injuries accounted for very few maltreatment-related admissions. Protocols that focus on high-risk injuries may miss the majority of maltreated children.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".