Aging with autism spectrum disorder: an emerging public health problem
Bibliographic record
Abstract
From 1943, when Leo Kanner originally described autism, and to the first objective criteria for "infantile autism" in DSM-III and the inclusion of Asperger's disorder in DSM-IV, the subsequent classification scheme for autistic disorders has led to a substantial change with the 2013 issuance of the DSM-5 by including subcategories into one umbrella diagnosis of autism spectrum disorder (ASD) (Baker, 2013). ASD is a lifelong neurodevelopmental disorder, characterized by social and communication impairments and restricted, stereotypical patterns of behavior (Baker, 2013). It is currently expected that most, or all of the actual cases of ASD, are identified in a timely way (i.e. in early childhood). However, there are many undiagnosed older adults who may have met the current diagnostic criteria for ASD as children, but never received such a diagnosis due to the fact it had yet to be established. In addition, some patients with relatively less impairing phenotypes may escape formal diagnosis in childhood, only to later be diagnosed in adulthood. Nevertheless, the first generation of diagnosed patients with ASD is now in old age. Many such ASD patients have needed family and institutional support for their lives subsequent to childhood diagnosis. Due to aging and death of their parents and other supportive figures leading to a loss of social structures, there is no better time than now for the medical community to act.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.016 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".