Twenty‐year outcome for individuals with autism and average or near‐average cognitive abilities
Bibliographic record
Abstract
Previous studies found substantial variability in adult outcome for people with autism whose cognitive functioning was within the near-average and average ranges. This study examined adult outcome for 41 such individuals (38 men and 3 women) originally identified through an epidemiological survey of autism in Utah. Mean age at the time of their previous cognitive assessment was 7.2 years (SD=4.1, range=3.1-25.9 years) and at follow-up was 32.5 years (SD=5.7 years, range=22.3-46.4 years). Outcome measures included standardized assessments of diagnostic status, cognitive ability, and adaptive behavior. Additional information collected concerned demographic variables, indicators of independence, social relationships, medical and psychiatric conditions, and social service use. Outcomes for this sample were better than outcomes described in previous work on individuals with similar cognitive functioning. For example, half of the participants were rated as "Very Good" or "Good" on a global outcome measure. As in previous studies, there was considerable variability in measured cognitive ability over time. Over half of the sample had large gains or losses of cognitive ability of greater than 1 standard deviation. Cognitive gain was associated with better outcome, as was better adaptive functioning. While all participants had baseline IQs in the nonimpaired range, there was limited evidence to support the use of other early childhood variables to predict adult outcome.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".