Optimal outcome in individuals with a history of autism
Bibliographic record
Abstract
BACKGROUND: Although autism spectrum disorders (ASDs) are generally considered lifelong disabilities, literature suggests that a minority of individuals with an ASD will lose the diagnosis. However, the existence of this phenomenon, as well as its frequency and interpretation, is still controversial: were they misdiagnosed initially, is this a rare event, did they lose the full diagnosis, but still suffer significant social and communication impairments or did they lose all symptoms of ASD and function socially within the normal range? METHODS: The present study documents a group of these optimal outcome individuals (OO group, n=34) by comparing their functioning on standardized measures to age, sex, and nonverbal IQ matched individuals with high-functioning autism (HFA group, n=44) or typical development (TD group, n=34). For this study, 'optimal outcome' requires losing all symptoms of ASD in addition to the diagnosis, and functioning within the nonautistic range of social interaction and communication. Domains explored include language, face recognition, socialization, communication, and autism symptoms. RESULTS: Optimal outcome and TD groups' mean scores did not differ on socialization, communication, face recognition, or most language subscales, although three OO individuals showed below-average scores on face recognition. Early in their development, the OO group displayed milder symptoms than the HFA group in the social domain, but had equally severe difficulties with communication and repetitive behaviors. CONCLUSIONS: Although possible deficits in more subtle aspects of social interaction or cognition are not ruled out, the results substantiate the possibility of OO from autism spectrum disorders and demonstrate an overall level of functioning within normal limits for this group.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".