Ethical dimensions of translational developmental neuroscience research in autism
Bibliographic record
Abstract
BACKGROUND: Since the 1990s, increasing research has been devoted to the identification of biomarkers for autism to help attain more objective diagnosis; enable early prediction of prognosis; and guide individualized intervention options. Early studies focused on the identification of genetic variants associated with autism, but more recently, research has expanded to investigate neurodevelopmental markers. While ethicists have extensively discussed issues around advances in autism genomics, much less ethical scrutiny has focused on research on early neurodevelopment and on the interventions being developed as a result. OBJECTIVES: We summarize the current state of the science on the identification of early markers for autism and its potential clinical applications, before providing an overview of the ethical issues arising from increasing understanding of children's neurodevelopment in very early life. RESULTS: Advances in the understanding of brain and behavioral trajectories preceding later autism diagnosis raise ethical concerns around three themes: (a) New models for understanding autism; (b) Risks and benefits of early identification and intervention; and (c) Communication of early concerns to families. These ethical issues should be further investigated in research conducted in partnership with autistic people and their families. CONCLUSIONS: This paper highlights the need for ethical scrutiny of early neurodevelopmental research in autism. Scrutiny requires expertise and methods from the basic sciences and bioethics, as well as constructive collaborations among autistic people, their parents, and autism researchers to anticipate early interventions that serve the community's interests and accommodate the varied experiences and preferences of people on the spectrum and their families.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.531 | 0.394 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.017 | 0.145 |
| Scholarly communication | 0.022 | 0.017 |
| Open science | 0.006 | 0.027 |
| Research integrity | 0.022 | 0.030 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".