Linking risk factors and outcomes in autism spectrum disorder: is there evidence for resilience?
Bibliographic record
Abstract
Autism spectrum disorder (referred to here as autism) is one of several overlapping neurodevelopmental conditions that have variable impacts on different individuals. This variability results from dynamic interactions between biological and non-biological risk factors, which result in increasing differentiation between individuals over time. Although this differentiation continues well into adulthood, the infancy period is when the brain and behavior develop rapidly, and when the first signs and symptoms of autism emerge. This review discusses advances in our understanding of the causal pathways leading to autism and overlapping neurodevelopmental conditions. Research is also mapping trajectories of brain and behavioral development for some risk groups, namely later born siblings of children with autism and/or infants referred because of developmental concerns. This knowledge has been useful in improving early identification and establishing the feasibility of targeted interventions for infant risk groups before symptoms arise. However, key knowledge gaps remain, such as the discovery of protective factors (biological or environmental) that may mitigate the impact of risk. Also, the dynamic mechanisms that underlie the associations between risk factors and outcomes need further research. These include the processes of resilience, which may explain why some individuals at risk for autism achieve better than expected outcomes. Bridging these knowledge gaps would help to provide tools for early identification and intervention that reflect dynamic developmental pathways from risk to outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".