Factors predicting general health concerns and atypical behaviours in children with prenatal alcohol exposure and other adverse exposures
Bibliographic record
Abstract
Background: Prenatal alcohol exposure (PAE) can have significant negative consequences on the health outcomes of children. Children with PAE often experience other prenatal and postnatal adverse exposures. Increased rates of general health concerns and atypical behaviours are seen in both children with PAE as well as with other patterns of adverse exposures, although these have not been systematically described. The association between multiple adverse exposures and adverse health concerns and atypical behaviours in children with PAE is unknown. Methods: = 22; 14 males, age range = 7.9-15.9 years) and their caregivers. Support vector machine learning classification models were used to predict the presence of health concerns and atypical behaviours based on adverse exposures. Associations between the sums of adverse exposures, health concerns, and atypical behaviours were examined using correlation analysis. Results: All children experienced health concerns, the most common being sensitivity to sensory inputs (64%; 14/22). Similarly, all children engaged in atypical behaviours, with atypical sensory behaviour (50%; 11/22) being the most common. Prenatal alcohol exposure was most important factor for predicting some health concerns and atypical behaviours, and alone and in combination with other factors. Simple associations between adverse exposures could not be identified for many health concerns and atypical behaviours. Conclusion: Children with PAE and other adverse exposures experience high rates of health concerns and atypical behaviours. This study demonstrates the complex effects of multiple adverse exposures on health and behaviour in 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".