Infants at risk for autism: a European perspective on current status, challenges and opportunities
Bibliographic record
Abstract
Currently, autism cannot be reliably diagnosed before the age of 2 years, which is why longitudinal studies of high-risk populations provide the potential to generate unique knowledge about the development of autism during infancy and toddlerhood prior to symptom onset. Early autism research is an evolving field in child psychiatric science. Key objectives are fine mapping of neurodevelopmental trajectories and identifying biomarkers to improve risk assessment, diagnosis and treatment. ESSEA (Enhancing the Scientific Study of Early Autism) is a COST (European Cooperation in Science and Technology) Action striving to create a European collaboration to enhance the progress of the discovery and treatment of the earliest signs of autism, and to establish European practice guidelines on early identification and intervention by bringing together European expertise from cognitive neuroscience and clinical sciences. The objective of this article is to clarify the state of current European research on at-risk autism research, and to support the understanding of different contexts in which the research is being conducted. We present ESSEA survey data on ongoing European high-risk ASD studies, as well as perceived challenges and opportunities in this field of research. We conclude that although high-risk autism research in Europe faces several challenges, the existence of several key factors (e.g., new and/or large-scale autism grants, availability of new technologies, and involvement of experienced research groups) lead us to expect substantial scientific and clinical developments in Europe in this field during the next few years.
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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 | 0.014 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".