Development of an individualized risk calculator for poor functioning in young people victimized during childhood: A longitudinal cohort study
Bibliographic record
Abstract
BACKGROUND: Childhood victimization elevates the average risk of developing functional impairment in adulthood. However, not all victimized children demonstrate poor outcomes. Although research has described factors that confer vulnerability or resilience, it is unknown if this knowledge can be translated to accurately identify the most vulnerable victimized children. OBJECTIVE: To build and internally validate a risk calculator to identify those victimized children who are most at risk of functional impairment at age 18 years. PARTICIPANTS: We utilized data from the Environmental Risk (E-Risk) Longitudinal Twin Study, a nationally-representative birth cohort of 2232 UK children born in 1994-95. METHODS: Victimization exposure was assessed repeatedly between ages 5 and 12 years along with a range of individual-, family- and community-level predictors. Functional outcomes were assessed at age 18 years. We developed and evaluated a prediction model for psychosocial disadvantage and economic disadvantage using the Least Absolute Shrinkage and Selection Operator (LASSO) regularized regression with nested 10-fold cross-validation. RESULTS: The model predicting psychosocial disadvantage following childhood victimization retained 12 of 22 predictors, had an area under the curve (AUC) of 0.65, and was well-calibrated within the range of 40-70% predicted risk. The model predicting economic disadvantage retained 10 of 22 predictors, achieved excellent discrimination (AUC = 0.80), and a high degree of calibration. CONCLUSIONS: Prediction modelling techniques can be applied to estimate individual risk for poor functional outcomes in young adulthood following childhood victimization. Such risk prediction tools could potentially assist practitioners to target interventions, which is particularly useful in a context of scarce resources.
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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 | 0.001 | 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".