Methodological challenges and opportunities when studying the course of autism
Bibliographic record
Abstract
Longitudinal research in autism has contributed a wealth of knowledge about etiological factors, development from childhood through adulthood, life course changes and needs, as well as longer-term adult outcomes for individuals on the spectrum and their family members. This research is essential to better understand the needs of individuals as they age. However, along with the as yet unrealized opportunities to understand an individual in more nuanced ways across time, there are challenges to utilizing this research design that should be considered. These include sample and measurement diversity, retention, outcome measures, analysis, and funding considerations. This article outlines some of the most pressing challenges together with potential solutions to maximize the value of longitudinal research designs that can help address questions that are of high priority to the autism community.Lay AbstractLongitudinal research has been critical to understand the life course of people with autism, including factors which increase the probability of an autism diagnosis, the emergence of early markers, co-occurring psychiatric conditions, predication of future educational and support needs across childhood and adulthood, and understanding what makes each person unique and contributes to the well-being of autistic people and their families. However, these studies take time, patience, investment of families and individuals, scientists and are challenging to all involved. This article will outline some of the issues that have occurred in the past and provide potential solutions to improve the quality of these studies to both the scientific and autistic communities. They include sample and measurement diversity, retention, outcome measures, analysis, and funding considerations. This understanding of the field is important for both scientific research and community engagement in the studies that include the autistic community.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".