94 Common Social Determinants of Health as Independent Predictors of Adverse Childhood Experiences and the Derivation of a Clinical Prediction Rule: Findings from a Longitudinal Quality Improvement Study
Bibliographic record
Abstract
Abstract Background Adverse Childhood Experiences (ACEs) are a group of early life events that lead to toxic stress and adverse adult health outcomes. Screening for ACEs can be challenging due to sensitivity and re-traumatization. There is a paucity of evidence regarding whether other social determinants of health (SDoH) might be independent predictors of an ACE score >=4. Likewise, no effective prediction rule exists for an elevated ACE score based on SDoH in children. Objectives 1) Identify independent predictors of elevated ACE score from commonly screened SDoH. 2) Derive a clinical prediction rule based on the available data. Design/Methods Data were drawn from a longitudinal quality improvement SDoH study in pediatric surgical clinics at a provincial children’s hospital. Primary outcome of interest was an ACE score >=4. Multivariable logistic regression was utilized to identify independent predictors among other SDoH. Prediction methods and ROC analyses were completed to derive a prediction rule. Results 515 respondents answered ACE screening; 63 (12.2%) reported >=4 ACEs. SDoH that were strong independent predictors of ACE score >=4 included poverty (OR 2.34, 95% CI 1.19-4.91), parental education (OR 2.76, 95% CI 1.17-6.54), and household income (OR 2.17, 95% CI 1.09-4.32). Housing status, Indigenous status, and disability status were not associated with elevated ACE score. A clinical prediction rule derived using four SDoH questions with a cut-off score of 1 had 96.67% sensitivity but only 21.54% specificity for an ACE score >=4 (AROC 0.75, 95% CI 0.69-0.81). Conclusion Several adverse SDoH were identified as independent predictors of an ACE score >=4 in children. A clinical prediction rule based on SDoH screening was sensitive but poorly specific for ACE >=4. Further research is required.
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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 | 0.028 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".