Autism Spectrum Disorders: Early Detection, Intervention, Education, and Psychopharmacological Management
Bibliographic record
Abstract
Our understanding and treatment of children with autism have changed dramatically since Leo Kanner first formally documented the disorder in 1943. With reference to the historical context, this paper reviews recent research addressing 4 major issues: early detection, intervention, education, and psychopharmacological management of children with autism and related (autistic) spectrum disorders (hereafter, "autism"). We conclude from our review of the evidence that, in the absence of additional, more compelling data, the clinical usefulness of existing screening instruments remains questionable. However, the potential importance of such research is underscored by the clear benefits of early behavioural intervention: despite differences in orientation, outcomes for children with autism can be significantly enhanced with early intensive intervention. Although many questions remain (notably, What are the critical therapeutic components? For whom? For what domains of development? For what level of intensity and duration?), interventions shown to be effective are all carefully planned, engineered, monitored, and designed to target specific skill domains. Including children with autism in regular classes within the public school system poses several challenges, the most pressing of which is the large number of school personnel who need to be trained in evidence-based teaching and behavioural management practices. Finally, psychotropic drugs may help to reduce some symptoms, but they are neither curative nor a substitute for other forms of support and intervention.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".