Trajectory research in children with an autism diagnosis: A scoping review
Bibliographic record
Abstract
Researchers increasingly employ longitudinal trajectory methods to understand developmental pathways of people on the autism spectrum across the lifespan. By assessing developmental or health-related outcome domains at three or more timepoints, trajectory studies can characterize their shape and varying rates of change over time. The purpose of this scoping review was to identify and summarize the published breadth of research that uses a trajectory study design to examine development in children (to age 18 years) diagnosed with autism. Using a systematic search and screening procedure, 103 studies were included. This review summarizes methodological characteristics across studies including the varying statistical approaches used. A series of figures maps where published research is available across 10 outcome domains and the ages over which children have been followed. Evidence gaps, informed by the perspectives of the autistic and caregiver stakeholders that were engaged in this review, are discussed. We recommend that future trajectory research addresses the absence of studies from low- and middle-income countries, considers longitudinal assessment of outcome domains that caregivers and autistic people consider meaningful, and plans follow-up periods with assessment timepoints that cover the gaps in ages where more outcome-specific data are needed. Lay Abstract The types of outcomes studied in children on the autism spectrum include clinical characteristics, such as social functioning, communication, language, or autism symptoms. Research that measures these outcomes at multiple timepoints is useful to improve our understanding of what to expect as children develop. In trajectory studies, researchers assess outcomes at three or more timepoints. This method has advantages over two-timepoint studies because it allows researchers to describe changes in the speed of development, such as accelerations, plateaus, or slowdowns. We identified and reviewed 103 published trajectory studies in children (to age 18 years) with an autism diagnosis. Importantly, we did not include studies of treatments or their effects, nor did we summarize the results of studies. Instead, this review summarizes the characteristics of the available published research, including the methods used, the many different outcomes that have been studied over time and the ages over which they have been studied. This summary may be of interest to autistic people and caregivers (parents) who want to know about the existence of research that provides answers about what to expect during an autistic child’s development. We have recommended that future trajectory research efforts try to make up for the lack of studies from low- and middle-income countries; that more attention is given to the following outcomes that are meaningful to caregivers and autistic people; and to try to fill in the age gaps where more outcome-specific data are needed.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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".